How AI Retrieves and Utilizes External Knowledge

Continuing from Day 2: AI’s Next Step After Storing Knowledge

In Day 2, we explored how AI converts external documents into embeddings and stores them in a vector database. But storing knowledge is just the first step. The real magic happens when AI retrieves the right knowledge at the right time to generate accurate responses.

?? Think of it like this: You’ve stored your study notes in different folders. But when it’s time for an exam, how do you find the most relevant notes quickly? That’s exactly what AI does—it searches its knowledge base, finds the best-matching information, and integrates it into its response.

?? Retrieval-Augmented Generation (RAG) in Action

Now that AI has external knowledge stored, it follows a three-step process to use that knowledge effectively:

1. Retrieval: AI searches its vector database to find relevant document chunks based on a query.
2. Augmentation: It combines the retrieved information with the user’s query.
3. Generation: AI then processes both inputs to generate a final, well-informed response.

This process ensures AI is not just making things up—it’s basing its response on real, stored knowledge.

Step 1: Understanding the User’s Query

When a user asks a question, AI first checks whether it needs external knowledge or if it can answer using its own training data.

?? Example:

• User asks: "What are the latest advancements in solar panel technology?"
• AI realizes that its training data is outdated, so it decides to retrieve recent knowledge from stored embeddings.

This decision-making ensures AI is always using the most accurate and up-to-date information.

Step 2: Retrieving Relevant Information from the Vector Database

Once AI decides that external knowledge is needed, it searches the vector database to find the best matches.

How does AI find relevant knowledge?

1. Vector Search: AI compares the user’s query embedding with stored embeddings in the vector database.
2. Cosine Similarity: AI measures how “close” the query is to each stored document chunk.
3. Selecting Top Matches: The most relevant document chunks are retrieved.

?? Example:

• AI receives the query: "Latest advancements in solar panel efficiency."
• It searches the vector database and finds the top 5 most relevant embeddings related to solar panel improvements.

Step 3: Augmenting Retrieved Information

Now that AI has retrieved the most relevant knowledge, it merges that knowledge with the user’s query before generating a response.

?? Process:

1. Contextual Augmentation: AI ensures the retrieved text is contextually aligned with the query.
2. Prompt Engineering: AI fine-tunes how it presents the response to make it clear and user-friendly.

?? Example:

• AI retrieves a chunk of text about "New breakthroughs in solar panel efficiency and materials."
• It integrates this information seamlessly into the response.

Step 4: Generating the Final Response

Now comes the final step—AI uses the retrieved knowledge to craft a response in natural language.

?? How AI generates the final response:

1. Uses Retrieved Knowledge – AI ensures the answer is based on reliable sources.
2. Refines the Response – AI eliminates unnecessary information and presents a concise, useful answer.
3. Adds Citations (If Needed) – Some implementations provide links to the original retrieved documents.

?? Example:

• AI responds: "The latest advancements in solar panels include improved photovoltaic efficiency, bifacial solar modules, and perovskite-based materials for higher energy conversion."

Challenges in AI Retrieval & How We Solve Them

Challenge 1: Hallucinations (AI Making Up Information)

? Solution: AI relies on retrieved facts instead of generating random responses.

Challenge 2: Outdated Information Retrieval

? Solution: Regularly update the vector database with the latest documents.

Challenge 3: Slow Retrieval Times

? Solution: Optimize vector searches using efficient indexing techniques like HNSW or IVF.

Final Thoughts & What’s Next?

Now we’ve completed the full process of retrieval, from storing external knowledge (Day 2) to retrieving it efficiently (Day 3). AI is now ready to generate more reliable and fact-based answers.

?? Coming Up in Day 4:

• How Standard RAG works step-by-step.
• What makes Standard RAG different from other AI retrieval techniques?
• Best practices to improve AI retrieval accuracy.

?? Your Turn: What’s one real-world scenario where AI retrieval could be useful? Share your thoughts in the comments! ??

Hinglish Version

AI Ka External Knowledge Retrieve Karne Ka Tarika

Day 2 Ke Baad: AI Ka Next Step Jab Usko Knowledge Ki Zarurat Hoti Hai

Day 2 me humne dekha ki AI external documents ko embeddings me convert karke vector database me store karta hai. Par sirf store karna kaafi nahi hota, jab AI ko kisi question ka jawab dena hota hai tab wo kaise relevant knowledge retrieve karta hai, wahi sabse important step hai!

?? Sochiye aapne apni padhai ke notes alag-alag folders me store kiye hain. Jab exam ka time aata hai to aap sabse zaroori notes kaise dhundhte hain? Bas AI bhi aise hi karta hai—wo apne knowledge base me search karta hai, sabse relevant information find karta hai aur apne response me use karta hai.

?? Retrieval-Augmented Generation (RAG) Kaise Kaam Karta Hai?

Jab AI ke paas external knowledge hota hai, tab wo is process ko follow karta hai:

1. Retrieval: AI apne vector database me se sabse relevant document chunks dhoondta hai.
2. Augmentation: AI user ki query aur retrieved information ko combine karta hai.
3. Generation: Fir AI in dono inputs ka use karke ek accurate aur meaningful response generate karta hai.

Is process se AI galat information generate nahi karta, balki real stored knowledge pe based response deta hai.

Step 1: AI User Ki Query Ko Samajhta Hai

Jab ek user koi sawaal puchta hai, AI pehle yeh decide karta hai ki kya usko external knowledge retrieve karne ki zarurat hai ya wo apni training data se hi jawab de sakta hai.

?? Example:

• User puchta hai: "Solar panel technology me naye advancements kya hain?"
• AI samajhta hai ki uske training data me latest details nahi hain, isliye wo stored embeddings me se relevant knowledge retrieve karega.

Yeh decision-making ensure karta hai ki AI hamesha up-to-date aur sahi information use kare.

Step 2: AI Relevant Information Retrieve Kaise Karta Hai?

Agar AI decide kare ki usko external knowledge chahiye, to wo vector database me search karta hai aur best match dhoondta hai.

AI retrieval process:

1. Vector Search: AI user ki query ka embedding create karta hai aur stored embeddings se compare karta hai.
2. Cosine Similarity: AI check karta hai ki query kaunse stored document chunks ke sabse kareeb hai.
3. Top Matches Select Karna: AI sabse relevant document chunks retrieve karta hai.

?? Example:

• AI ko query mili: "Solar panel efficiency improve karne ke naye tareeke."
• AI apne vector database me search karta hai aur sabse relevant 5 embeddings retrieve karta hai jo solar panel technology ke naye improvements se related hain.

Step 3: Retrieved Information Ko Augment Karna

Ab AI ne sabse relevant knowledge retrieve kar liya, ab wo isko user ki query ke saath merge karta hai taki final response coherent aur relevant ho.

?? Process:

1. Contextual Augmentation: AI ensure karta hai ki retrieved text user ki query se match kare.
2. Prompt Engineering: AI response ko structured aur easy-to-understand banata hai.

?? Example:

• AI ne ek document chunk retrieve kiya jo "Solar panels ki efficiency badhane ke naye materials aur techniques" pe based hai.
• AI isko user ki query ke saath integrate karke ek meaningful answer prepare karta hai.

Step 4: Final Response Generate Karna

Ab AI retrieved knowledge ka use karke ek natural language me structured response generate karta hai.

?? Final Response Generation Process:

1. AI Retrieved Knowledge Ka Use Karta Hai – AI ensure karta hai ki answer reliable sources pe based ho.
2. Response Ko Refine Karta Hai – AI unnecessary information hata deta hai aur ek concise aur useful answer present karta hai.
3. Citations Bhi Add Kar Sakta Hai – Kuch AI implementations references bhi provide karti hain.

?? Example:

• AI respond karega: "Solar panel efficiency improve karne ke naye tareekon me photovoltaic efficiency enhancement, bifacial solar modules, aur perovskite-based materials ka use kiya ja raha hai."

AI Retrieval Ke Challenges Aur Solutions

Challenge 1: AI Kabhi Kabhi Galat Information (Hallucinations) Generate Kar Sakta Hai

? Solution: AI ko ensure karna hoga ki wo retrieved facts pe based answer de, bina apni taraf se kuch invent kiye.

Challenge 2: AI Kabhi Outdated Information Retrieve Kar Sakta Hai

? Solution: Regular updates se ensure karein ki vector database me hamesha latest documents stored hon.

Challenge 3: Retrieval Speed Slow Ho Sakti Hai

? Solution: Efficient indexing techniques jaise HNSW ya IVF ka use karein taki fast retrieval possible ho.

Final Thoughts & Next Steps

Ab humne retrieval process ko complete kar diya hai, jisme Day 2 me knowledge store karna aur Day 3 me usko retrieve karke AI ka response improve karna cover kiya.

?? Day 4 me hum dekhenge:

• Standard RAG kaise kaam karta hai aur uske steps kya hain.
• Standard RAG kaise doosre AI retrieval techniques se alag hai?
• AI retrieval accuracy improve karne ke best practices.

?? Aapke Vichar:AI retrieval ka real-world me sabse useful use-case kya ho sakta hai? Apni thoughts comments me share karein! ??

Previous Article From The Series

How AI Understands and Stores Extra Knowledge Read the full article here

What is RAG? Simplifying AI’s Secret Sauce for Smarter Answers Read the full article here