In Part 2, we learned why AI sometimes hallucinates. One of the biggest reasons is that an LLM can only answer based on what it learned during training and the information available in its context window. We also introduced grounding-providing the model with reliable information at runtime instead of expecting it to know everything.

But that raises an important question: Where does that information come from? Modern AI applications don't simply dump an entire database or a thousand-page PDF into the prompt. Instead, they first identify the most relevant pieces of information and only send those to the model. In this article, we'll learn how that works.

Running Example

Let's continue building our AI-powered Travel Planner. So far, it can answer general travel questions using the knowledge it learned during training. Now we want to make it much smarter by uploading several documents into our application:

Lonely Planet's Japan travel guide
A PDF containing train schedules
A document listing recommended local restaurants
Hotel information
Internal travel policies for our company

Together, these documents contain hundreds of pages. Now the user asks:

I'm staying near Tokyo Station. Which ramen restaurant from our travel guide is within walking distance and is known for vegetarian options?

Somewhere in those hundreds of pages is the answer. The challenge is no longer generating text-it's finding the right information first.

The Problem: An LLM Can't Read Your Entire Knowledge Base Every Time

A common misconception is that AI applications simply send all their documents to the model. Imagine our travel guide contains 450 pages, thousands of restaurant listings, hotel descriptions, transportation details, and sightseeing recommendations. Sending all of that to the LLM every time someone asks "Where should I eat tonight?" creates several problems.

First, many documents are simply too large to fit inside the model's context window. Second, even if they did fit, making the model read hundreds of irrelevant pages just to answer a simple question is expensive, slow, and inefficient. Instead, we want to send only the information that matters. The question becomes: How do we find the right few paragraphs from hundreds of pages?

Your first instinct might be: "Why not just search the documents?" That's exactly what we'll explore next.

Keyword Search: The Traditional Approach

Before AI became popular, most applications relied on keyword search. The idea is simple: the user searches for a word, and the application finds documents containing that exact word.

For example, given this travel guide text:

Tokyo has several excellent vegetarian ramen restaurants...
Mount Fuji is one of Japan's most iconic landmarks...
The Shinkansen connects Tokyo and Kyoto...

If the user searches for "vegetarian ramen", the search engine finds the first paragraph because it contains both words. While traditional keyword search is simple, fast, and easy to understand, it falls short when matching meaning instead of exact text.

When Keyword Search Falls Short

Synonyms and Meaning (Example 1): If a user asks "Where can I eat meat-free ramen?" and our document says "Vegetarian ramen is available near Tokyo Station", a traditional keyword search may completely miss this result because it searches for exact words, not meaning. It doesn't know that meat-free, vegetarian, and plant-based are closely related concepts.
Name Variations (Example 2): If a user asks to "Suggest a place near Fujisan", and the document says "A day trip to Mount Fuji is highly recommended", keyword search often fails because it doesn't understand that Fujisan and Mount Fuji refer to the same place.
Terminology Differences (Example 3): If a user asks "Where can I find authentic local noodle shops?" and the guide says "Several ramen restaurants popular with residents...", keyword search fails to connect noodle shop with ramen restaurant.

The real limitation is that keyword search compares text, while humans compare meaning. This is why search systems often return dozens of irrelevant results or miss the best result entirely, as you've likely experienced when searching documentation using a word that differs from the author's chosen terminology.

Semantic Search: Searching by Meaning

This is where AI changes everything. Instead of asking "Does this document contain the same words?", semantic search asks "Does this document mean the same thing?"

Let's return to our example. The user asks "Where can I eat meat-free ramen near Tokyo Station?" and the travel guide says "Ichiran offers vegetarian ramen a short walk from Tokyo Station." A semantic search understands that meat-free and vegetarian express nearly the same idea, allowing it to retrieve the correct passage even though the exact words are different.

But computers don't naturally understand meaning; they understand numbers. How can a computer determine that vegetarian and meat-free are closely related? That's where embeddings come in.

Embeddings: Turning Meaning into Numbers

An embedding is a numerical representation of text that captures its meaning. Think of it as a fingerprint-not of the exact words, but of what those words represent.

For example, these three phrases all have different wording but express similar ideas, so their embeddings end up being very similar:

Vegetarian ramen
Meat-free noodles
Plant-based ramen

Conversely, phrases like Mount Fuji, Flight booking, and Currency exchange produce embeddings that are quite different because they represent completely unrelated concepts. The key takeaway is: embeddings represent meaning, not exact wording.

Why Not Compare the Original Text?

Let's go back to our Travel Planner. Suppose our travel guide contains:

Ichiran offers vegetarian ramen near Tokyo Station.

Now imagine two different users:

User A: Recommend vegetarian ramen.
User B: Where can I find meat-free noodle restaurants?

A computer comparing plain text sees very few matching words between User B's query and the guide. A traditional keyword search might decide they are unrelated. Embeddings solve this by converting both pieces of text into numerical representations where similar meanings end up close together.

Semantic Similarity: Measuring Meaning Instead of Words

Once text is converted into embeddings, comparing sentences becomes much easier. Instead of asking if they contain the same words, we ask: How similar are their embeddings? If two embeddings are close together, they are likely talking about similar ideas; if they are far apart, they are probably unrelated.

A useful mental model is to imagine every sentence placed on a giant map. Similar ideas appear close together, while unrelated concepts are placed farther apart:

                  Travel

                      ●
                Visit Japan

           ●                   ●
  Vegetarian Ramen      Tokyo Station

                ●
         Meat-free Noodles


--------------------------------------------


                      ●
              JavaScript Closures

                                ●
            Docker Networking

Notice how the travel-related concepts naturally cluster together, while completely unrelated software concepts appear elsewhere. This is exactly what embeddings help achieve.

Cosine Similarity: Finding the Closest Match

Now that every sentence has an embedding, we need a way to compare them mathematically. One of the most common techniques is cosine similarity.

Suppose our Travel Planner has these restaurant descriptions stored:

A: Vegetarian ramen near Tokyo Station.
B: Traditional sushi restaurant.
C: Italian pizza in Osaka.

If the user asks for "Meat-free noodles near Tokyo Station", the embeddings might compare like this:

Restaurant	Similarity
Vegetarian ramen near Tokyo Station	0.96
Traditional sushi restaurant	0.41
Italian pizza in Osaka	0.09

The higher the score, the closer the meanings. Notice that the user never mentioned the word vegetarian, yet Restaurant A is still identified as the best match because the meaning is similar.

Mental Model

In a library, keyword search works like looking up every book containing the word ramen. Semantic search works by finding books discussing the same idea, even if they use completely different words.

Where Do These Embeddings Come From?

Fortunately, you don't have to calculate these numbers yourself. Just as LLMs specialize in generating text, there are embedding models that specialize in generating embeddings. Instead of producing a paragraph, an embedding model takes text and produces a list of numbers representing its meaning:

User Text ("Vegetarian ramen...") ──► Embedding Model ──► [0.18, -0.42, 0.91, ...]

The resulting list (or vector) usually contains hundreds or thousands of numbers. You never need to interpret them yourself; their only purpose is to make mathematical similarity comparisons possible.

Chunking: Breaking Large Documents into Pieces

How do we generate embeddings for a 500-page travel guide? We don't feed the entire book into the embedding model at once. Embedding models have strict input limit constraints (typically measured in tokens), and comparing a whole book's meaning is too vague to be useful. If a user asks about a specific ramen restaurant, we want to retrieve the exact paragraph discussing it, not a 500-page book embedding.

So, we break the document down into smaller, logical pieces in a process called chunking. A chunk could be a single paragraph, a section of 100-200 words, or a sliding window of text (e.g., 200 words with a 20-word overlap to ensure context isn't cut off at boundaries).

Mental Model

Think of chunking like cutting a long movie reel into short, thematic scenes. If you want to find the scene with Mount Fuji, it is much easier to search and index short clips than searching the entire three-hour film.

💡 Developer's Takeaway

Choosing the right chunk size is a balancing act. If chunks are too small, they might lose context (e.g., a sentence saying "It is delicious" without mentioning what is delicious). If they're too large, the specific details get averaged out, and search accuracy drops.

Vector Databases: Storing and Indexing Meanings

Once we have broken our travel guide into thousands of chunks and generated an embedding for each, we need a place to store them. Traditional databases (like PostgreSQL or MySQL) are optimized for finding exact matches like numbers, dates, or strings. They aren't designed to compare thousands of high-dimensional lists of numbers to find which ones are "closest" in meaning.

That's where Vector Databases come in. A vector database is designed specifically to store and index high-dimensional vectors (embeddings) and search through them based on mathematical similarity (like cosine similarity) extremely fast.

Common vector database setups include:

Dedicated vector databases (Pinecone, Milvus, Qdrant, Chroma)
Vector extensions for traditional databases (like pgvector for PostgreSQL)

💡 Developer's Takeaway

You don't need to write complex search algorithms yourself. A vector database handles the indexing and allows you to query it using an embedding, returning the most semantically similar chunks in milliseconds.

Embedding Dimensions: Understanding the Scale

When you configure a vector database, it will ask you to specify the number of dimensions. Dimensions refer to the size of the list of numbers generated by the embedding model. For example:

A lightweight model might output vectors with 384 dimensions.
A standard model (like OpenAI's text-embedding-3-small) outputs 1536 dimensions.
Large models can output 3072 dimensions or more.

Each dimension represents an abstract feature of meaning that the model learned during training. You don't need to know what each dimension represents; the database only cares that you are comparing vectors of the exact same size.

💡 Developer's Takeaway

An embedding model and a vector database must match: you cannot store a 1536-dimension embedding in a database index configured for 384 dimensions.

RAG (Retrieval-Augmented Generation): Putting It Together

Now we have all the pieces to solve our Travel Planner's original challenge: answering questions using our travel guides without sending the entire book to the LLM. We use a pattern called RAG (Retrieval-Augmented Generation).

RAG combines Retrieval (finding relevant chunks from our vector database) and Generation (having the LLM write a response using those chunks). Here is the step-by-step workflow:

Ingestion (Happens once in advance):
- Break documents into chunks.
- Generate embeddings for each chunk.
- Store the chunks and their embeddings in a vector database.
Querying (Happens at runtime):
- User asks: "Recommend a vegetarian ramen shop near Tokyo Station."
- The application generates an embedding for the user's question.
- The application queries the vector database using this question embedding.
- The database returns the top 2 or 3 most similar text chunks.
- The application merges the user's question and the retrieved chunks into a single prompt (grounding context).
- The LLM reads the context and generates a correct, verified answer.

               User Question
                     │
                     ▼
             Generate Embedding
                     │
                     ▼
    Query Vector DB ──► Stored Chunks
                     │
                     ▼
            Get Relevant Chunks
                     │
                     ▼
    Build Prompt (Question + Chunks)
                     │
                     ▼
             LLM Generates Text
                     │
                     ▼
             Grounded Response

💡 Developer's Takeaway

RAG is the most popular way to connect proprietary data to LLMs. It ensures the model's response is grounded in facts, reduces hallucinations, and works without needing to retrain the model.

CAG (Cache-Augmented Generation): The New Alternative

While RAG is powerful, it requires setting up chunking pipelines, embedding models, and vector databases. This can be complex. With modern LLMs having massive context windows (often supporting 1 million to 2 million tokens), another approach has emerged: CAG (Cache-Augmented Generation).

Instead of retrieving matching chunks at runtime, CAG preloads the entire document set (e.g., our 500-page travel guide) directly into the model's context window. It leverages KV (Key-Value) Caching to precompute and cache the mathematical states of the documents once. When a user asks a question, the LLM reads the cached documents instantly, bypassing the retrieval pipeline entirely.

RAG is like looking up a topic in a library catalog, retrieving three specific books, and reading them to find the answer.
CAG is like having the entire encyclopedia already open on your desk with the pages instantly readable.

💡 Developer's Takeaway

CAG simplifies the architecture by eliminating vector databases and chunking. However, it is limited by the size of the context window and is best suited for relatively static datasets that fit comfortably within the model's limit. RAG remains essential for massive or constantly changing data.

Recap

In this article, we explored how to give AI knowledge beyond its training. We covered:

Keyword Search vs. Semantic Search
Embeddings, Semantic Similarity, and Cosine Similarity
Chunking, Vector Databases, and Embedding Dimensions
RAG (Retrieval-Augmented Generation) and CAG (Cache-Augmented Generation)

In Part 4, we will answer the next logical question: How do we connect our model to the outside world so it can execute tasks, use external APIs, and remember users over time? We will explore tool calling, memory, Model Context Protocol (MCP), and AI agents.

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