This is just a talk about Gen AI done with Claude for Twistag

How do AI models really work with information?

The Training Limitation

Language models are trained on massive datasets, but once training is done:

1. Their knowledge is frozen in time
2. They can't directly access external information
3. They work purely through pattern recognition

Model training date: 2023
Question: "Who won the 2024 Super Bowl?"
Response: "I apologize, but I don't have access to information about events after 2023."

Common Misconception: "AI models can search the internet or your files"
Reality: They can only work with what's in their training data or explicitly provided in the prompt

UNDERSTANDING VECTOR SEARCH

How do we find relevant information?

Converting Text to Numbers

Text → Vector Embeddings (high-dimensional number arrays)

"cat" → [0.2, -0.5, 0.8, ...]
"kitten" → [0.3, -0.4, 0.7, ...]
"database" → [-0.8, 0.1, -0.3, ...]

Similar meanings = Similar vectors
"cat" is closer to "kitten" than to "database" in vector space

This is how we find relevant documents:
1. Convert search query to vector
2. Find documents with similar vectors
3. Return the most relevant matches

RAG IN ACTION

Retrieval-Augmented Generation: Step by Step

1. Query
"Write a proposal for Google"

→

2. Search
Find relevant docs

→

3. Augment
Add to prompt

→

4. Generate
Create response

# 1. Initial query vector
query_vector = embed("Write a proposal for Google")

# 2. Find similar documents
relevant_docs = vector_search(query_vector)

# 3. Create enhanced prompt
prompt = f"""Using these documents as reference: {relevant_docs} Write a proposal following our guidelines."""

# 4. Generate with context
response = model.generate(prompt)

Key Understanding:
- Not training or learning
- Just smart document retrieval + context-aware generation
- Like giving reference materials to a knowledgeable person

PRACTICAL APPLICATION

Seeing It All Come Together

Without RAG

Q: "What was our Q4 revenue?"
A: "I don't have access to your company's financial data."

With RAG

1. Find Q4 report
2. Add to context
3. Generate informed response
A: "Based on the Q4 report, revenue was $10.2M"

RAG makes AI useful for specific tasks by:
1. Finding relevant information in real-time
2. Adding it to the conversation context
3. Letting the model use both its training and your data

THE BIG PICTURE

How it All Comes Together

Smart Lookup + AI Understanding = Powerful Results