Ready, Run, RAG: Your Data, Your AI, Your Rules
An executive's guide to building cutting edge GenAI applications with Retrieval-Augmented Generation (RAG)
Two essential building blocks for any Generative AI product.
To build a generative AI product, you need two key elements.
Generation: This is where the AI crafts responses by deciphering vast datasets.
Source: It's the origin of data, the foundation from which the AI draws insights.
Chatbots like ChatGPT are a perfect example. They receive questions, fetch answers from their foundational data, and craft responses in natural language.
RAG solves the proprietary data problem.
But what if you want to use your own exclusive data source?
What if you want to leverage Generative AI for your own data lake? Or connect multiple data streams to gain valuable insights?
Enter RAG – Retrieval-Augmented Generation. RAG allows you to generate natural language responses on your proprietary data.
While RAG is still new, it already boasts promising use cases across industries.
The three most popular use cases are,
Customer Service Chatbots: RAG empowers chatbots with real-time data, enhancing customer interactions in areas like order status, shipping times, FAQs, and product availability. The sky is the limit here.
Business Intelligence Tools: RAG aids in retrieving insights from vast datasets. Examples include reading product roadmaps (yours truly), analyzing markets, optimizing supply chains, forecasting sales, etc.
Personalized Learning Platforms: Organizations are leveraging RAG to generate customized learning programs. Platforms like Khan Academy deploy RAG as a personal tutor, and others are creating tailored coaching programs.
And this is just the beginning. I promise you this list will only grow :)
RAG’s true power is in its retrieval capabilities.
RAG works in the background, between your query and the LLM.
When a user inputs a prompt (steps #1 and #2), RAG does the following:
Retrieval of Contextual Information: Based on the query, RAG retrieves relevant information from a data source (#3 and #4).
Augmentation of User's Prompt (step #5): The user's original prompt is augmented with the retrieved information and sent to the Large Language Model (LLM).
Response Generated by LLM: The LLM generates a response based on the augmented prompt (#6), which is returned to the user (#7).
The magic lies in RAG’s in it’s retrieval process. Where it can scour the data repository (vector database) for relevant text chunks, augment the original prompt with this new information, and send a more robust prompt to the LLM.
RAG offers three critical advantages.
So why does all this matter?
Because RAG allows you to
Control your data source: You choose the data source, enabling your AI to leverage proprietary data rather than default foundational knowledge.
Improve accuracy and reduce hallucinations: RAG minimizes inaccuracies by grounding responses in your data, reducing the likelihood of the LLM generating false information
Provide real-time answers: Since RAG applications pull from a live data source, the information returned is always current. No more outdated knowledge.
Getting started with RAG.
As RAG is still in its infancy, here are practical tips to initiate exploration.
Identify Use Cases: Clarify where RAG could enhance an existing process or enable a new application.
Explore Tools: Investigate frameworks like HuggingFace, LlamaIndex, LangChain, Dust, and REALM to understand capabilities.
Start Small: Run pilot projects on focused use cases to grasp RAG nuances iteratively.
Build a Prototype: Create a simple interface for hands-on RAG experimentation.
Iterate and Expand: Use lessons learned to tackle more ambitious use cases.
As you navigate the evolving landscape of AI, RAG offers a pragmatic solution.
Happy Building!!
PS: I just launched a 4-week bootcamp on how PMs can use AI to:
Achieve tasks 10x faster (writing requirements, crafting metrics, synthesizing feedback, etc.)
Deliver higher quality work, and
Accelerate the product development process
This is the same playbook I use to coach PMs to improve their productivity and build great products.
If you know of any PMs who might be interested, please forward this note or they can reach out to me directly.
Course Link: AI Bootcamp for Product Managers