June 16, 2025 • 33 mins
Artificial intelligence is undergoing a profound transformation. Gone are the days when AI could only work with what it had memorized during training. Two revolutionary approaches are now reshaping what's possible: Retrieval Augmented Generation (RAG) and Reasoning and Acting (ReAct).
RAG gives AI systems the equivalent of an open-book exam. Instead of relying solely on pre-trained knowledge, these systems can search external databases, documents, or even the internet in real-time to retrieve the most current and relevant information. This solves two critical problems: outdated knowledge and hallucinations (when AI confidently makes up information). By grounding responses in verifiable facts and providing citations, RAG creates more trustworthy AI interactions across industries - from customer support bots that pull exact policy information to medical assistants that access the latest research.
Meanwhile, ReAct takes AI capabilities further by enabling step-by-step problem-solving. Rather than generating answers in one go, ReAct-based systems think through problems methodically, alternating between reasoning and taking actions like performing calculations or searching for information. This mirrors how humans tackle complex challenges, breaking them down into manageable steps and gathering resources along the way. The result is AI that can solve multi-step problems transparently, showing its work and building trust.
These technologies are spawning exciting innovations like autonomous AI agents that can break down high-level goals into actionable tasks, and frameworks that make these capabilities easier to implement in real-world applications. From scheduling appointments to researching complex topics and even making online purchases, future AI assistants will engage with the world on our behalf through natural conversations.
Want to understand how these game-changing technologies work in plain language? Tune in to discover how RAG and ReAct are creating AI that doesn't just inform but truly assists - grounded in reliable knowledge and capable of sophisticated reasoning and action.
Want to join a community of AI learners and enthusiasts? AI Ready RVA is leading the conversation and is rapidly rising as a hub for AI in the Richmond Region. Become a member and support our AI literacy initiatives.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome back to Inspire AI, the podcast where we
explore the cutting edge ofartificial intelligence in an
accessible way.
I'm your host, jason McGintyfrom AI Ready RVA, and today
we're diving into twogame-changing concepts in AI
(00:20):
Retrieval, augmented GenerationRAG for short and REACT short
for Reasoning and Acting.
These might sound liketechnical buzzwords, but stick
with me.
They are revolutionizing how AIsystems work, making them more
knowledgeable, reliable and evenaction-oriented.
(00:44):
Knowledgeable, reliable andeven action-oriented.
Imagine an AI that can look upfacts on the fly and take
actions to solve problems.
That's what RAG and React areall about, and in this episode,
we'll break down what thesetechnologies are in plain
language, highlighting thereal-world applications, from
smarter customer support toadvanced search engines and
(01:07):
autonomous agents, and discusshow they're shaping the future
of AI.
We'll also touch on somerelated innovations like
Toolformer, autogpt, landgraphand DSPy, comparing them to RAG
and React.
Whether you're a casuallistener curious about where AI
(01:30):
is headed, or a techprofessional looking for deeper
insight, we've got you coveredwith an educational yet engaging
exploration.
So let's get started.
All right, let's start withretrieval augmented generation
RAG for short.
Consider the court system whereyou have a judge and a court
(01:52):
clerk.
The AI model is like the judge,gains knowledge by sending the
court clerk to fetch relevantinformation from an external
library, which it then uses toproduce a more informed answer.
Retrieval Augmented Generation,or RAG, is essentially about
(02:13):
giving AI access to the externalknowledge source so it can
ground its answers in real facts.
In simple terms, a RAG systemcombines a language model like
GPT-401, whatever right, that'sthe part of the AI that
generates text With a retrievalsystem, the part that fetches
(02:36):
facts or documents.
This means that, instead ofrelying only on whatever the AI
model memorized during training,it can search a library or
database on the fly to get thelatest and the most important,
most relevant and more detailedinformation it needs.
One researcher even likened itto the difference between a
(02:59):
closed book exam and an openbook exam, where the AI can look
up answers in a book.
Naturally, an AI with an openbook exam where the AI can look
up answers in a book Naturallyan AI with an open book is going
to be more accurate on specific, up-to-date questions, right?
So the term RAG was coined in2020 from a research paper out
of Meta AI, and today it's agrowing family of methods
(03:24):
adopted across the industry.
It solves several problems Ifyou've used chatbots, like
earlier versions of ChatGPT, youprobably noticed two big issues
Out-of-date knowledge andhallucinations.
Rag directly addresses both.
Raag directly addresses both bypulling in information from a
(04:02):
relevant source at query time.
It ensures the model has accessto the most current, reliable
facts instead of being stuckwith only what it learned last
year as an example.
Also, because the AI can citeor show the retrieved evidence,
you, as the user, can verify theanswer against sources, which
helps build more trust right.
In short, rag augments agenerative AI model with a
real-time knowledge lookup,dramatically improving the
accuracy and relevancy of itsresponses.
(04:24):
So, crucially, rag helpsprevent the AI from guessing
when it doesn't know something.
One Stack Overflow article putit quite nicely Every language
model has a cutoff to itstraining knowledge and tends to
confidently improvise when askedabout facts it hasn't seen.
(04:45):
Rag introduces a retrieval stepto fill those gaps with real
data.
If you've ever chatted with abot that knew about yesterday's
news or could discuss a documentyou provided, chances are you
were interacting with aRAG-powered system.
For example, openai's ChatGPTcan use a browsing plugin to
(05:09):
fetch current information.
That's RAG in action.
So, similarly, enterprisechatbots can be connected to
company databases so that whenyou ask what's our travel
reimbursement policy or anythinglike that, the bot actually
looks up the policy document andgives the answer grounded in
(05:31):
that text.
No more making up things, itquotes the manual directly.
Real world applications of RAGare burgeoning across many
fields.
Essentially, any scenario whereup-to-date or specialized
information is needed is a goodfit.
Think about customer support, aprime example.
(05:53):
Companies are using RAG-basedassistance to handle support
queries by retrieving answersfrom product guides or internal
wikis, so customers can getaccurate, sighted responses
instead of generic guesses oreven having people spend a lot
of extra time on digging throughthe resources themselves.
(06:15):
And in the medical field, adoctor could query an AI that's
augmented with medical index orjournal database.
It would retrieve the latestresearch and patient guidelines
to inform its answer.
Likewise, financial analystscan ask an AI assistant linked
to live market data for thenewest trends or reports.
(06:39):
And here's another everydayopportunity for RAG search
engines.
Clearly, the new wave of searchassistants like Bing or others
use RAG to provide directanswers with references,
effectively turning web searchinto a conversation backed by
real-time retrieval.
In fact, rag is so useful thatmajor tech players like IBM,
(07:05):
microsoft, google, aws, nvidiaare all integrating it into
their AI offerings.
Ibm's own Watson platformincludes RAG to keep enterprise
AI answers up to date withoutconstant retraining.
The beauty of RAG is that it'smodular.
You can plug any knowledgesource into an AI model, whether
(07:29):
it's your personal notes, acorporate database or the entire
internet, the AI can consultthat source when answering.
Think about it.
This opens doors to AI thatwould behave like an expert
assistant in virtually anydomain, as long as you give it
the right knowledge base, ofcourse.
(07:50):
Now we're gonna dig into react,aka reasoning plus acting.
It's a step-by-step approachfor AI to think and act.
So let's start with what is it?
Think about an LLM poweredautonomous agent.
Such an agent uses planningmodule to break down tasks,
(08:14):
integrates with external toolsor APIs to act on the world or
fetch info.
It maintains memory of paststeps.
React style prompting allowsthe AI to alternate between
thought, which is your reasoning, and natural language, and
(08:37):
action, or calling a tool right,then observing the result
before the next thought.
React, short for reasoning andacting, is a paradigm that
enables AI models, especiallylarge language models, to not
only reason through problems,but also take actions to reach a
(08:58):
solution In simpler terms,instead of an AI just spitting
out an answer in one go.
A React-based AI will think outloud, step by step, and at
certain points it can perform anaction, such as calling an
external tool or running acalculation, maybe even
(09:22):
searching for information, andthen use the result of that
action to inform its next step.
This approach is inspired byhow humans solve complex
problems.
For instance, we might breakdown a problem into its subtasks
, do some research orcalculations in between and
(09:43):
gradually work towards theanswer.
React teaches AI to do the sameby interleaving reasoning steps
, or the thoughts, with actionsteps, where it takes action.
So let's break that down withan example.
Suppose you ask a React-enabledAI how many prime numbers are
(10:05):
there between 2,500 and 3,000?
A regular AI might try to solvethis one in one shot and
potentially make an error orguess.
A React agent, however, wouldstart by reasoning.
It would say to itself first, Ishould find all the primes in
(10:26):
that range.
I have a tool to do mathcalculations, so let me use that
.
That's the thought.
Then it issues an action.
For instance, call a primefinding tool or script for that
range.
It gets back the list of primes, so it's making an observation
(10:46):
and then reasons again Now Ishould count how many primes are
in the list.
It may then use the calculatortool again or simply count in
reasoning and finally give youthe answer with the correct
count.
Throughout this process, the AIis effectively figuring out the
steps, not unlike how a humanwould, combining knowledge,
(11:11):
reasoning and tool use.
That's the essence of React theAI reasons about what, observes
the result and then continuesreasoning A loop of thought and
action.
So why is this a big deal?
(11:33):
Because it significantlyextends what AI can do.
Traditional LLMs, if they don'tknow a fact or can't compute
something internally, are stuck,but a React agent can recognize
these moments and do somethingabout it.
It can look up the needed fact,like RAG, or do a calculation,
(12:00):
or even interact with asimulated environment.
In technical terms, react turnsa language model into an agent
that can interact with the worldbeyond just chat.
As one Google research blog putit.
This paradigm allows languagemodels to handle not only
(12:21):
reasoning tasks, which helpfiguring things out, but
decision-making tasks, whereit's actively choosing actions,
yielding better performance thandoing either one alone.
In fact, experiments have shownthat React prompting can
outperform models that only dochain-of-thought reasoning or
(12:43):
only act without reasoning, thatonly do chain-of-thought
reasoning or only act withoutreasoning by having the model
generate explicit reasoningtraces.
Side note, chain of thought.
Prompting is a promptengineering technique that aims
to improve language modelsperformance on tasks requiring
(13:03):
logic, calculation and decisionmaking by structuring the input
prompt in a way that mimicshuman reasoning.
So, as a user, you might saydescribe your reasoning in steps
or explain your answer step bystep.
You might append that to yourquery at the end, just before
(13:25):
inferencing the large languagemodel.
In essence, this promptingtechnique asks the LLM to not
only generate a result but alsodetail the series of
intermittent steps that led tothat answer.
But anyway, by having the modelgenerate explicit reasoning
traces, or thoughts and explicitactions, we also get a side
(13:50):
benefit interpretability.
We can follow the model's chainof thought and see which
actions it took, which is muchmore transparent than a single
black box answer.
This makes it easier todiagnose errors and increases
trust, since the process issomewhat auditable, right, many
(14:12):
of us in highly regulatedindustries really like this.
So some real-world applicationsof React often overlap with what
people call LLM agents or AIagents.
Essentially, anytime you hearabout an AI that can use tools
(14:33):
or autonomously performmulti-step tasks, there's likely
a react approach under the hood.
For instance, consider advancedcustomer support bots that not
only retrieve information usingrag, but can also ask the user
(14:53):
clarifying questions and updatea database record.
These bots are doing reasoning,deciding to ask a follow-up and
acting making the databaseupdate.
Another example is in AI codingassistance.
A tool like OpenAI's CodeInterpreter plugin allowed
(15:15):
ChatGPT to execute Python code.
A React-based coding assistantcould take a user's request,
then internally write and runcode action to get an answer,
like generating a plot orchecking a condition, then
return the result.
This is exactly how some codingassistants debug and verify
(15:38):
their outputs.
Perhaps the most buzzed aboutuse of React is in the realm of
autonomous AI agents that cancarry out high-level tasks.
There's a recent exampleAutoGPT, which emerged in 2023
as an experimental open sourceproject where an AI agent tries
(16:00):
to fulfill a broad goal bybreaking it into steps and
chaining many reasoning actioncycles together.
For example, if you tellAutoGPT, help me grow my podcast
audience, it can generate aplan, identify tasks like
improve website SEO, find socialmedia strategies, then proceed
(16:25):
to execute those tasks byGoogling information, writing
content or calling APIs,iterating until it runs out of
ideas or reaches the goal.
Autogpt garnered a lot ofattention as a glimpse of what
fully autonomous AI workersmight look like, even though, in
(16:49):
practice, it often got confusedor stuck.
It's a reminder that the techis still very early.
It's a reminder that the techis still very early.
Nonetheless, it showed theworld the potential of
React-style agents, which areagents that can iterate,
experiment and operate somewhatindependently to solve complex
(17:09):
problems.
Companies are now exploringsuch agents for things like
workflow automation.
Such agents for things likeworkflow automation.
Imagine an AI agent handling anentire expense report process,
from receiving a receipt,extracting data, entering it
into the system and sending areimbursement request all by
(17:34):
itself.
Nvidia describes LLM-basedagents as ideal for tasks like
smart chatbots, automated codegeneration and process
automation, precisely becausethey can use tools and plan
actions, not just chat, fromscheduling meetings by talking
(17:57):
to your calendar to controllingIoT.
That's the Internet of Things.
Devices with voice commandswhere the AI decides which
device API to call.
React is enabling a new wave ofAI that doesn't just answer
questions but gets things done.
Now I want to talk about someemerging technologies and
(18:21):
similar approaches.
As RAG and React have risen toprominence, a host of similar or
complementary technologies haveemerged.
These aim to push theboundaries of what AI can do by
extending the idea of tool useand reasoning.
Let's look at a few notableones and how they compare.
Toolformer from Meta, a methodthat literally teaches a
(18:47):
language model to use tools byitself.
Researchers at Meta found a wayto train an LLM to decide which
APIs to call, then to call them, and how to incorporate the
results into its answer, all ina self-supervised fashion.
The motivation is similar toRAG and React Base LLMs are
(19:10):
great with language, butstruggle with things like
arithmetic or up-to-date facts,which simpler tools can handle
easily.
Tool Farmer bridges that gap,achieving the best of both
worlds by letting the modeloffload certain tasks to
external tools like a calculatoror a search engine translator,
(19:34):
and then merge the results backinto the response.
In essence, it's a trainedversion of what React does,
without prompting.
The model learns when and howto act.
The result was improvedperformance on many tasks,
matching larger models'abilities by using tools, all
(19:54):
without sacrificing the languagemodel's original skills.
I spoke about AutoGPT before.
It's an open-source projectthat became the poster child for
autonomous AI agents.
Autogpt is built on OpenAI'sGPT-4 and was designed to
automate multi-step projects andcomplex workflows with minimal
(20:18):
human input.
You give it a high-level goaland it will break that goal into
subtasks, prioritize them andtackle them one by one, creating
something like a to-do list foritself.
Under the hood, autogpt enablesa loop of reasoning and acting
very much in line with Reactprinciples.
(20:40):
It can use plugins to accessthe internet or other apps,
could self-chain its outputs tocontinuously work on a problem.
(21:02):
While AutoGPT often struggledwith consistency and sometimes
went in circles a commonchallenge for unrestrained AI
agents it sparked a hugeinterest in agentic AI.
It's basically a showcase ofReact-style prompting taken to
the extreme, where the AIcreates its own plan and tools
(21:26):
usage to meet a broad objective.
This concept has led to manyspinoffs and inspired frameworks
for building your own agents.
In fact, after AutoGPT wentviral, we saw a surge of
projects and research intomaking such agents more reliable
.
(21:47):
Next is Langraph.
So this is a newer frameworkfrom makers of Langchain which
helps design and manage complexAI agent workflows.
If React is about how an agentthinks and acts stepwise,
langraph is about structuringthe overall process.
(22:07):
It uses a graph-basedarchitecture to lay out an AI
workflow as a network of nodesand edges, to lay out an AI
workflow as a network of nodesand edges, where each node could
be a step or an action and theedges define the flow or
decision paths In plain language.
Laingraph lets developerscreate a map of an AI agent's
(22:32):
tasks.
You can specify branches, loops, dependencies between subtasks
and attach language modelreasoning or tools at each node.
This makes it easier to buildagents that have to handle
complex decision trees ormulti-step processes in a robust
way.
The graph approach bringstransparency and modularity, so
(22:58):
one can monitor the agent stateat each node, kind of like
checking its notebook as itworks, and adjust the workflow
by tweaking the graph structure.
Langraph isn't a competing ideato RAG and React, but rather an
orchestration tool.
It can incorporate RAG andReact, but rather an
orchestration tool.
(23:18):
It can incorporate RAG forknowledge lookup or React-style
nodes for tool use, all within acontrolled graph.
Think of it as giving thedeveloper a higher level control
panel to visualize and managean AI agent's reasoning paths,
which is especially useful forenterprise settings where
(23:40):
reliability and traceability arekey.
Finally, we have DSPy, standingfor declarative self-improving
Python.
Dspy is an open sourceframework from Stanford that
tackles AI development fromanother angle.
While RAG and React are focusedon the AI's capabilities, dspy
(24:03):
focuses on the developerexperience of building AI
systems.
Dspy focuses on the developerexperience of building AI
systems.
It allows you to program LLMbehaviors using code modules
instead of hard-to-maintainprompt scripts.
In current LLM applications, alot of effort goes into writing
prompts and chaining modelstogether, often using frameworks
(24:29):
like Langchain.
This can lead to what some callprompt spaghetti Complex,
brittle logic scattered acrossprompts.
Dspy's solution is to letdevelopers write modular Python
code that declares what the AIshould do, for example, define a
retrieval step and thenreasoning step, then a tool call
(24:53):
, etc.
Etc.
And the framework handlestranslating that into optimized
prompts.
Under the hood, it's likemoving from assembly language to
a high-level language.
You describe the AI pipeline ata high level and DSPy worries
about the prompt engineeringdetails.
(25:14):
So in context of our discussion, dspy can be seen as a way to
implement things like RAG orReact more robustly.
Instead of manually rewriting aprompt with a dozen examples, a
developer could use DSPy tocompose a reasoning module and a
(25:36):
tool-using module and let theframework figure out the optimal
prompting.
This trend reflects a maturingindustry.
As techniques like RAG andReact become essential, tools
like DSPy are emerging to makebuilding production-quality AI
systems easier and lesserror-prone.
(25:56):
All of these technologiesToolformer, autogpt, landgraf,
dspy and others areinterconnected in the sense that
they're advancing the idea ofAI that is both knowledgeable
and action-capable.
They build upon the foundationsof RAG and React.
For instance, transformer andAutoGPT are direct descendants
(26:20):
of the React philosophy, whileLandgraf and DSPy are part of
the ecosystem enabling andmanaging those advanced
abilities in real-worldapplications.
The key takeaway is that the AIcommunity is actively tackling
the limitations of vanilla AImodels by connecting them with
(26:42):
tools, knowledge bases andstructured reasoning strategies.
It's a very exciting time, withresearch and practice informing
each other.
New ideas like React spawn newtools like these talked about,
which in turn, make it easier todeploy even more sophisticated
AI assistance.
So as we look ahead, it's clearthat technologies like RAG and
(27:07):
React are shaping the future ofAI in profound ways.
Not long ago, we thought of AIassistance as either all-knowing
or completely useless.
If a question fell outsidetheir training data, you got a
blank stare or a wrong answer,and if a task required multiple
(27:30):
steps, the AI simply couldn'thandle it in one go.
Now, with retrievalaugmentation, ai systems can
continually learn and staycurrent without needing to be
retrained from scratch.
They can also provide evidencefor their answers, like links
and excerpts, which is crucialfor trust.
(27:52):
Meanwhile, the React paradigmand the rise of AI agents show a
path forward toward AI that canengage with the world, not just
generate text.
This means future AI couldschedule your appointments, do
your shopping online,troubleshoot your software or
(28:16):
even conduct research forscouring databases, all through
a natural conversation with youwhere the AI transparently walks
through the steps.
Industry trends already point tothis direction.
As mentioned, all the big cloudproviders and AI companies are
incorporating RAG to make theirmodels more reliable and
(28:38):
enterprise ready.
Some have called retrievalaugmentation and tools like it
the future of generative AI,because it addresses fundamental
weaknesses, like hallucinations, that have so far limited to
the adoption of AI inhigh-stakes areas.
Similarly, there's enormousmomentum behind the idea of AI
(29:00):
agents.
Countless startups and researchlabs are working on improving
autonomous AI, decision-making,planning and tool use.
Since the splash of AutoGPT,we've seen better frameworks,
evaluations and even governanceapproaches for agentech AI.
We should expect upcoming AIsystems whether it's the next
(29:24):
Siri, alexa or a businessautomation tool to leverage
these capabilities.
They won't operate as amonolithic black box.
They shouldn't anyway.
Instead, they'll be hybridsPart knowledge retriever, part
reasoner, part executor.
This hybrid approach is how weget closer to AI that behaves
(29:48):
intelligently in a human-likesense it can recall facts when
needed, break down problems, useinstruments to get things done
and explain its thought processFor the general public.
What this means is more usefuland trustworthy AI in everyday
(30:08):
life.
Imagine customer service botsthat actually solve your issue,
because they can look up youraccount information and company
policies, thanks to RAG, andperform actions like issuing a
refund, thanks to React, all inone interaction.
(30:29):
Or even personal assistantsthat don't just set reminders
but can handle complex choreslike planning a trip by
researching destinations,comparing prices and even
booking the tickets.
For you and for the technicalfolks and AI professionals
(30:50):
listening, the message is thatthe tool-using, reasoning AI
paradigm is here to stay andit's evolving fast.
With frameworks like LANGRAPHand DSPy, plus ongoing
improvements and promptingstrategies, it's becoming easier
to build sophisticated AIsystems that were practically
(31:13):
impossible just a couple ofyears ago.
Of course, there are challengesto iron out.
React-based agents need to bemade more robust and reliable.
Ensuring safety is alsoparamount.
When you let an AI actautonomously, you need
guardrails so it doesn't dosomething unintended.
(31:36):
Likewise, rag systems are onlyas good as the data sources they
have, curating and updatingthose knowledge bases will be an
ongoing task.
Nonetheless, the trajectory isset.
We are moving from a worldwhere using AI meant phrasing a
(31:57):
question and hoping the singularmodel in the cloud knows the
answer to a world where using AImeans engaging a dynamic
problem solver.
This AI will fetch information,perform intermediate
computations and interact withvarious services to help you,
(32:20):
much like a human assistantwould, but at digital speed and
scale.
Retrieval, augmented generationand React are two pillars of
this new generation of AI.
One makes the AI knowledgeableand up to date.
(32:43):
The other makes it active andprocess driven.
Together they are enabling AIsystems that can truly assist
and not just inform.
To close us out, it is aninspiring time in AI For anyone
worried that AI was just a fancyautocomplete that sometimes
fibs, rag and React show that weare actively in engineering our
way past those limitations.
(33:06):
By grounding AI in realknowledge and giving it the
ability to reason and act, weare turning these models into
something much, much morepowerful and useful.
So the next time you useAI-powered app and it cites a
(33:26):
source or completes acomplicated task for you, you'll
know a bit about the clevertechniques behind the scenes
that made it possible.
So thank you for tuning in tothis episode of Inspire AI.
We hope you learned somethingnew about retrieval, augmented
generation, react and the futureof AI agents.
Until next time, stay curious,stay informed and keep
(33:48):
innovating.
Welcome back to Inspire AI, the podcast where we
explore the cutting edge ofartificial intelligence in an
accessible way.
I'm your host, jason McGintyfrom AI Ready RVA, and today
we're diving into twogame-changing concepts in AI
(00:20):
Retrieval, augmented GenerationRAG for short and REACT short
for Reasoning and Acting.
These might sound liketechnical buzzwords, but stick
with me.
They are revolutionizing how AIsystems work, making them more
knowledgeable, reliable and evenaction-oriented.
(00:44):
Knowledgeable, reliable andeven action-oriented.
Imagine an AI that can look upfacts on the fly and take
actions to solve problems.
That's what RAG and React areall about, and in this episode,
we'll break down what thesetechnologies are in plain
language, highlighting thereal-world applications, from
smarter customer support toadvanced search engines and
(01:07):
autonomous agents, and discusshow they're shaping the future
of AI.
We'll also touch on somerelated innovations like
Toolformer, autogpt, landgraphand DSPy, comparing them to RAG
and React.
Whether you're a casuallistener curious about where AI
(01:30):
is headed, or a techprofessional looking for deeper
insight, we've got you coveredwith an educational yet engaging
exploration.
So let's get started.
All right, let's start withretrieval augmented generation
RAG for short.
Consider the court system whereyou have a judge and a court
(01:52):
clerk.
The AI model is like the judge,gains knowledge by sending the
court clerk to fetch relevantinformation from an external
library, which it then uses toproduce a more informed answer.
Retrieval Augmented Generation,or RAG, is essentially about
(02:13):
giving AI access to the externalknowledge source so it can
ground its answers in real facts.
In simple terms, a RAG systemcombines a language model like
GPT-401, whatever right, that'sthe part of the AI that
generates text With a retrievalsystem, the part that fetches
(02:36):
facts or documents.
This means that, instead ofrelying only on whatever the AI
model memorized during training,it can search a library or
database on the fly to get thelatest and the most important,
most relevant and more detailedinformation it needs.
One researcher even likened itto the difference between a
(02:59):
closed book exam and an openbook exam, where the AI can look
up answers in a book.
Naturally, an AI with an openbook exam where the AI can look
up answers in a book Naturallyan AI with an open book is going
to be more accurate on specific, up-to-date questions, right?
So the term RAG was coined in2020 from a research paper out
of Meta AI, and today it's agrowing family of methods
(03:24):
adopted across the industry.
It solves several problems Ifyou've used chatbots, like
earlier versions of ChatGPT, youprobably noticed two big issues
Out-of-date knowledge andhallucinations.
Rag directly addresses both.
Raag directly addresses both bypulling in information from a
(04:02):
relevant source at query time.
It ensures the model has accessto the most current, reliable
facts instead of being stuckwith only what it learned last
year as an example.
Also, because the AI can citeor show the retrieved evidence,
you, as the user, can verify theanswer against sources, which
helps build more trust right.
In short, rag augments agenerative AI model with a
real-time knowledge lookup,dramatically improving the
accuracy and relevancy of itsresponses.
(04:24):
So, crucially, rag helpsprevent the AI from guessing
when it doesn't know something.
One Stack Overflow article putit quite nicely Every language
model has a cutoff to itstraining knowledge and tends to
confidently improvise when askedabout facts it hasn't seen.
(04:45):
Rag introduces a retrieval stepto fill those gaps with real
data.
If you've ever chatted with abot that knew about yesterday's
news or could discuss a documentyou provided, chances are you
were interacting with aRAG-powered system.
For example, openai's ChatGPTcan use a browsing plugin to
(05:09):
fetch current information.
That's RAG in action.
So, similarly, enterprisechatbots can be connected to
company databases so that whenyou ask what's our travel
reimbursement policy or anythinglike that, the bot actually
looks up the policy document andgives the answer grounded in
(05:31):
that text.
No more making up things, itquotes the manual directly.
Real world applications of RAGare burgeoning across many
fields.
Essentially, any scenario whereup-to-date or specialized
information is needed is a goodfit.
Think about customer support, aprime example.
(05:53):
Companies are using RAG-basedassistance to handle support
queries by retrieving answersfrom product guides or internal
wikis, so customers can getaccurate, sighted responses
instead of generic guesses oreven having people spend a lot
of extra time on digging throughthe resources themselves.
(06:15):
And in the medical field, adoctor could query an AI that's
augmented with medical index orjournal database.
It would retrieve the latestresearch and patient guidelines
to inform its answer.
Likewise, financial analystscan ask an AI assistant linked
to live market data for thenewest trends or reports.
(06:39):
And here's another everydayopportunity for RAG search
engines.
Clearly, the new wave of searchassistants like Bing or others
use RAG to provide directanswers with references,
effectively turning web searchinto a conversation backed by
real-time retrieval.
In fact, rag is so useful thatmajor tech players like IBM,
(07:05):
microsoft, google, aws, nvidiaare all integrating it into
their AI offerings.
Ibm's own Watson platformincludes RAG to keep enterprise
AI answers up to date withoutconstant retraining.
The beauty of RAG is that it'smodular.
You can plug any knowledgesource into an AI model, whether
(07:29):
it's your personal notes, acorporate database or the entire
internet, the AI can consultthat source when answering.
Think about it.
This opens doors to AI thatwould behave like an expert
assistant in virtually anydomain, as long as you give it
the right knowledge base, ofcourse.
(07:50):
Now we're gonna dig into react,aka reasoning plus acting.
It's a step-by-step approachfor AI to think and act.
So let's start with what is it?
Think about an LLM poweredautonomous agent.
Such an agent uses planningmodule to break down tasks,
(08:14):
integrates with external toolsor APIs to act on the world or
fetch info.
It maintains memory of paststeps.
React style prompting allowsthe AI to alternate between
thought, which is your reasoning, and natural language, and
(08:37):
action, or calling a tool right,then observing the result
before the next thought.
React, short for reasoning andacting, is a paradigm that
enables AI models, especiallylarge language models, to not
only reason through problems,but also take actions to reach a
(08:58):
solution In simpler terms,instead of an AI just spitting
out an answer in one go.
A React-based AI will think outloud, step by step, and at
certain points it can perform anaction, such as calling an
external tool or running acalculation, maybe even
(09:22):
searching for information, andthen use the result of that
action to inform its next step.
This approach is inspired byhow humans solve complex
problems.
For instance, we might breakdown a problem into its subtasks
, do some research orcalculations in between and
(09:43):
gradually work towards theanswer.
React teaches AI to do the sameby interleaving reasoning steps
, or the thoughts, with actionsteps, where it takes action.
So let's break that down withan example.
Suppose you ask a React-enabledAI how many prime numbers are
(10:05):
there between 2,500 and 3,000?
A regular AI might try to solvethis one in one shot and
potentially make an error orguess.
A React agent, however, wouldstart by reasoning.
It would say to itself first, Ishould find all the primes in
(10:26):
that range.
I have a tool to do mathcalculations, so let me use that
.
That's the thought.
Then it issues an action.
For instance, call a primefinding tool or script for that
range.
It gets back the list of primes, so it's making an observation
(10:46):
and then reasons again Now Ishould count how many primes are
in the list.
It may then use the calculatortool again or simply count in
reasoning and finally give youthe answer with the correct
count.
Throughout this process, the AIis effectively figuring out the
steps, not unlike how a humanwould, combining knowledge,
(11:11):
reasoning and tool use.
That's the essence of React theAI reasons about what, observes
the result and then continuesreasoning A loop of thought and
action.
So why is this a big deal?
(11:33):
Because it significantlyextends what AI can do.
Traditional LLMs, if they don'tknow a fact or can't compute
something internally, are stuck,but a React agent can recognize
these moments and do somethingabout it.
It can look up the needed fact,like RAG, or do a calculation,
(12:00):
or even interact with asimulated environment.
In technical terms, react turnsa language model into an agent
that can interact with the worldbeyond just chat.
As one Google research blog putit.
This paradigm allows languagemodels to handle not only
(12:21):
reasoning tasks, which helpfiguring things out, but
decision-making tasks, whereit's actively choosing actions,
yielding better performance thandoing either one alone.
In fact, experiments have shownthat React prompting can
outperform models that only dochain-of-thought reasoning or
(12:43):
only act without reasoning, thatonly do chain-of-thought
reasoning or only act withoutreasoning by having the model
generate explicit reasoningtraces.
Side note, chain of thought.
Prompting is a promptengineering technique that aims
to improve language modelsperformance on tasks requiring
(13:03):
logic, calculation and decisionmaking by structuring the input
prompt in a way that mimicshuman reasoning.
So, as a user, you might saydescribe your reasoning in steps
or explain your answer step bystep.
You might append that to yourquery at the end, just before
(13:25):
inferencing the large languagemodel.
In essence, this promptingtechnique asks the LLM to not
only generate a result but alsodetail the series of
intermittent steps that led tothat answer.
But anyway, by having the modelgenerate explicit reasoning
traces, or thoughts and explicitactions, we also get a side
(13:50):
benefit interpretability.
We can follow the model's chainof thought and see which
actions it took, which is muchmore transparent than a single
black box answer.
This makes it easier todiagnose errors and increases
trust, since the process issomewhat auditable, right, many
(14:12):
of us in highly regulatedindustries really like this.
So some real-world applicationsof React often overlap with what
people call LLM agents or AIagents.
Essentially, anytime you hearabout an AI that can use tools
(14:33):
or autonomously performmulti-step tasks, there's likely
a react approach under the hood.
For instance, consider advancedcustomer support bots that not
only retrieve information usingrag, but can also ask the user
(14:53):
clarifying questions and updatea database record.
These bots are doing reasoning,deciding to ask a follow-up and
acting making the databaseupdate.
Another example is in AI codingassistance.
A tool like OpenAI's CodeInterpreter plugin allowed
(15:15):
ChatGPT to execute Python code.
A React-based coding assistantcould take a user's request,
then internally write and runcode action to get an answer,
like generating a plot orchecking a condition, then
return the result.
This is exactly how some codingassistants debug and verify
(15:38):
their outputs.
Perhaps the most buzzed aboutuse of React is in the realm of
autonomous AI agents that cancarry out high-level tasks.
There's a recent exampleAutoGPT, which emerged in 2023
as an experimental open sourceproject where an AI agent tries
(16:00):
to fulfill a broad goal bybreaking it into steps and
chaining many reasoning actioncycles together.
For example, if you tellAutoGPT, help me grow my podcast
audience, it can generate aplan, identify tasks like
improve website SEO, find socialmedia strategies, then proceed
(16:25):
to execute those tasks byGoogling information, writing
content or calling APIs,iterating until it runs out of
ideas or reaches the goal.
Autogpt garnered a lot ofattention as a glimpse of what
fully autonomous AI workersmight look like, even though, in
(16:49):
practice, it often got confusedor stuck.
It's a reminder that the techis still very early.
It's a reminder that the techis still very early.
Nonetheless, it showed theworld the potential of
React-style agents, which areagents that can iterate,
experiment and operate somewhatindependently to solve complex
(17:09):
problems.
Companies are now exploringsuch agents for things like
workflow automation.
Such agents for things likeworkflow automation.
Imagine an AI agent handling anentire expense report process,
from receiving a receipt,extracting data, entering it
into the system and sending areimbursement request all by
(17:34):
itself.
Nvidia describes LLM-basedagents as ideal for tasks like
smart chatbots, automated codegeneration and process
automation, precisely becausethey can use tools and plan
actions, not just chat, fromscheduling meetings by talking
(17:57):
to your calendar to controllingIoT.
That's the Internet of Things.
Devices with voice commandswhere the AI decides which
device API to call.
React is enabling a new wave ofAI that doesn't just answer
questions but gets things done.
Now I want to talk about someemerging technologies and
(18:21):
similar approaches.
As RAG and React have risen toprominence, a host of similar or
complementary technologies haveemerged.
These aim to push theboundaries of what AI can do by
extending the idea of tool useand reasoning.
Let's look at a few notableones and how they compare.
Toolformer from Meta, a methodthat literally teaches a
(18:47):
language model to use tools byitself.
Researchers at Meta found a wayto train an LLM to decide which
APIs to call, then to call them, and how to incorporate the
results into its answer, all ina self-supervised fashion.
The motivation is similar toRAG and React Base LLMs are
(19:10):
great with language, butstruggle with things like
arithmetic or up-to-date facts,which simpler tools can handle
easily.
Tool Farmer bridges that gap,achieving the best of both
worlds by letting the modeloffload certain tasks to
external tools like a calculatoror a search engine translator,
(19:34):
and then merge the results backinto the response.
In essence, it's a trainedversion of what React does,
without prompting.
The model learns when and howto act.
The result was improvedperformance on many tasks,
matching larger models'abilities by using tools, all
(19:54):
without sacrificing the languagemodel's original skills.
I spoke about AutoGPT before.
It's an open-source projectthat became the poster child for
autonomous AI agents.
Autogpt is built on OpenAI'sGPT-4 and was designed to
automate multi-step projects andcomplex workflows with minimal
(20:18):
human input.
You give it a high-level goaland it will break that goal into
subtasks, prioritize them andtackle them one by one, creating
something like a to-do list foritself.
Under the hood, autogpt enablesa loop of reasoning and acting
very much in line with Reactprinciples.
(20:40):
It can use plugins to accessthe internet or other apps,
could self-chain its outputs tocontinuously work on a problem.
(21:02):
While AutoGPT often struggledwith consistency and sometimes
went in circles a commonchallenge for unrestrained AI
agents it sparked a hugeinterest in agentic AI.
It's basically a showcase ofReact-style prompting taken to
the extreme, where the AIcreates its own plan and tools
(21:26):
usage to meet a broad objective.
This concept has led to manyspinoffs and inspired frameworks
for building your own agents.
In fact, after AutoGPT wentviral, we saw a surge of
projects and research intomaking such agents more reliable
.
(21:47):
Next is Langraph.
So this is a newer frameworkfrom makers of Langchain which
helps design and manage complexAI agent workflows.
If React is about how an agentthinks and acts stepwise,
langraph is about structuringthe overall process.
(22:07):
It uses a graph-basedarchitecture to lay out an AI
workflow as a network of nodesand edges, to lay out an AI
workflow as a network of nodesand edges, where each node could
be a step or an action and theedges define the flow or
decision paths In plain language.
Laingraph lets developerscreate a map of an AI agent's
(22:32):
tasks.
You can specify branches, loops, dependencies between subtasks
and attach language modelreasoning or tools at each node.
This makes it easier to buildagents that have to handle
complex decision trees ormulti-step processes in a robust
way.
The graph approach bringstransparency and modularity, so
(22:58):
one can monitor the agent stateat each node, kind of like
checking its notebook as itworks, and adjust the workflow
by tweaking the graph structure.
Langraph isn't a competing ideato RAG and React, but rather an
orchestration tool.
It can incorporate RAG andReact, but rather an
orchestration tool.
(23:18):
It can incorporate RAG forknowledge lookup or React-style
nodes for tool use, all within acontrolled graph.
Think of it as giving thedeveloper a higher level control
panel to visualize and managean AI agent's reasoning paths,
which is especially useful forenterprise settings where
(23:40):
reliability and traceability arekey.
Finally, we have DSPy, standingfor declarative self-improving
Python.
Dspy is an open sourceframework from Stanford that
tackles AI development fromanother angle.
While RAG and React are focusedon the AI's capabilities, dspy
(24:03):
focuses on the developerexperience of building AI
systems.
Dspy focuses on the developerexperience of building AI
systems.
It allows you to program LLMbehaviors using code modules
instead of hard-to-maintainprompt scripts.
In current LLM applications, alot of effort goes into writing
prompts and chaining modelstogether, often using frameworks
(24:29):
like Langchain.
This can lead to what some callprompt spaghetti Complex,
brittle logic scattered acrossprompts.
Dspy's solution is to letdevelopers write modular Python
code that declares what the AIshould do, for example, define a
retrieval step and thenreasoning step, then a tool call
(24:53):
, etc.
Etc.
And the framework handlestranslating that into optimized
prompts.
Under the hood, it's likemoving from assembly language to
a high-level language.
You describe the AI pipeline ata high level and DSPy worries
about the prompt engineeringdetails.
(25:14):
So in context of our discussion, dspy can be seen as a way to
implement things like RAG orReact more robustly.
Instead of manually rewriting aprompt with a dozen examples, a
developer could use DSPy tocompose a reasoning module and a
(25:36):
tool-using module and let theframework figure out the optimal
prompting.
This trend reflects a maturingindustry.
As techniques like RAG andReact become essential, tools
like DSPy are emerging to makebuilding production-quality AI
systems easier and lesserror-prone.
(25:56):
All of these technologiesToolformer, autogpt, landgraf,
dspy and others areinterconnected in the sense that
they're advancing the idea ofAI that is both knowledgeable
and action-capable.
They build upon the foundationsof RAG and React.
For instance, transformer andAutoGPT are direct descendants
(26:20):
of the React philosophy, whileLandgraf and DSPy are part of
the ecosystem enabling andmanaging those advanced
abilities in real-worldapplications.
The key takeaway is that the AIcommunity is actively tackling
the limitations of vanilla AImodels by connecting them with
(26:42):
tools, knowledge bases andstructured reasoning strategies.
It's a very exciting time, withresearch and practice informing
each other.
New ideas like React spawn newtools like these talked about,
which in turn, make it easier todeploy even more sophisticated
AI assistance.
So as we look ahead, it's clearthat technologies like RAG and
(27:07):
React are shaping the future ofAI in profound ways.
Not long ago, we thought of AIassistance as either all-knowing
or completely useless.
If a question fell outsidetheir training data, you got a
blank stare or a wrong answer,and if a task required multiple
(27:30):
steps, the AI simply couldn'thandle it in one go.
Now, with retrievalaugmentation, ai systems can
continually learn and staycurrent without needing to be
retrained from scratch.
They can also provide evidencefor their answers, like links
and excerpts, which is crucialfor trust.
(27:52):
Meanwhile, the React paradigmand the rise of AI agents show a
path forward toward AI that canengage with the world, not just
generate text.
This means future AI couldschedule your appointments, do
your shopping online,troubleshoot your software or
(28:16):
even conduct research forscouring databases, all through
a natural conversation with youwhere the AI transparently walks
through the steps.
Industry trends already point tothis direction.
As mentioned, all the big cloudproviders and AI companies are
incorporating RAG to make theirmodels more reliable and
(28:38):
enterprise ready.
Some have called retrievalaugmentation and tools like it
the future of generative AI,because it addresses fundamental
weaknesses, like hallucinations, that have so far limited to
the adoption of AI inhigh-stakes areas.
Similarly, there's enormousmomentum behind the idea of AI
(29:00):
agents.
Countless startups and researchlabs are working on improving
autonomous AI, decision-making,planning and tool use.
Since the splash of AutoGPT,we've seen better frameworks,
evaluations and even governanceapproaches for agentech AI.
We should expect upcoming AIsystems whether it's the next
(29:24):
Siri, alexa or a businessautomation tool to leverage
these capabilities.
They won't operate as amonolithic black box.
They shouldn't anyway.
Instead, they'll be hybridsPart knowledge retriever, part
reasoner, part executor.
This hybrid approach is how weget closer to AI that behaves
(29:48):
intelligently in a human-likesense it can recall facts when
needed, break down problems, useinstruments to get things done
and explain its thought processFor the general public.
What this means is more usefuland trustworthy AI in everyday
(30:08):
life.
Imagine customer service botsthat actually solve your issue,
because they can look up youraccount information and company
policies, thanks to RAG, andperform actions like issuing a
refund, thanks to React, all inone interaction.
(30:29):
Or even personal assistantsthat don't just set reminders
but can handle complex choreslike planning a trip by
researching destinations,comparing prices and even
booking the tickets.
For you and for the technicalfolks and AI professionals
(30:50):
listening, the message is thatthe tool-using, reasoning AI
paradigm is here to stay andit's evolving fast.
With frameworks like LANGRAPHand DSPy, plus ongoing
improvements and promptingstrategies, it's becoming easier
to build sophisticated AIsystems that were practically
(31:13):
impossible just a couple ofyears ago.
Of course, there are challengesto iron out.
React-based agents need to bemade more robust and reliable.
Ensuring safety is alsoparamount.
When you let an AI actautonomously, you need
guardrails so it doesn't dosomething unintended.
(31:36):
Likewise, rag systems are onlyas good as the data sources they
have, curating and updatingthose knowledge bases will be an
ongoing task.
Nonetheless, the trajectory isset.
We are moving from a worldwhere using AI meant phrasing a
(31:57):
question and hoping the singularmodel in the cloud knows the
answer to a world where using AImeans engaging a dynamic
problem solver.
This AI will fetch information,perform intermediate
computations and interact withvarious services to help you,
(32:20):
much like a human assistantwould, but at digital speed and
scale.
Retrieval, augmented generationand React are two pillars of
this new generation of AI.
One makes the AI knowledgeableand up to date.
(32:43):
The other makes it active andprocess driven.
Together they are enabling AIsystems that can truly assist
and not just inform.
To close us out, it is aninspiring time in AI For anyone
worried that AI was just a fancyautocomplete that sometimes
fibs, rag and React show that weare actively in engineering our
way past those limitations.
(33:06):
By grounding AI in realknowledge and giving it the
ability to reason and act, weare turning these models into
something much, much morepowerful and useful.
So the next time you useAI-powered app and it cites a
(33:26):
source or completes acomplicated task for you, you'll
know a bit about the clevertechniques behind the scenes
that made it possible.
So thank you for tuning in tothis episode of Inspire AI.
We hope you learned somethingnew about retrieval, augmented
generation, react and the futureof AI agents.
Until next time, stay curious,stay informed and keep
(33:48):
innovating.
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