TODO

• https://www.youtube.com/watch?v=0Zr3NwcvpA0
• similarity search as an alternative to vector embeddings

fine tuning is taking a pre-existing model and training it further with your own data set.
you might want to fine tune instead of DAG

• You want consistent tone/format (e.g., legal summaries, internal policies)
• You have lots of repetitive tasks and want more consistency
• Prompt engineering isn’t enough
• You want to reduce token usage (less prompt length)

resources

• Andrej Karpathy Youtube and following him in general
  • ex director of AI @ Tesla. His presentation on computer vision helped me a lot in thesis
  • Deep dive into LLMs like ChatGPT
• Deep Dive into LLMs like ChatGPT (Andrej)

AI Tools concepts

AI Agents concepts

top lvl goals and challenges

• reduce hallucinations

Governance & Guardrails

Prompts are reviewed for bias, hallucination risk, safety, and regulatory compliance.
Enterprises often apply structured fallback prompts if the primary one fails.

Prompt Chaining

Multi-step workflows where outputs from one prompt are fed into the next (e.g., extract → classify → summarize).

tools

LangChain / LlamaIndex: Useful for chaining prompts, managing context, building apps on top of LLMs.

Embedding and Vector DB

Embedding

is a numerical representation of data (e.g., text) in vector form—usually a list of floating-point numbers.
The goal: capture the meaning or semantic similarity of content.
Two similar texts will have embeddings that are close to each other in vector space.

🔍 Example:
"Hello world" → [0.12, -0.44, 0.88, ...] # A 1536-dimension vector
"Hi there" → [0.10, -0.40, 0.85, ...] # Very similar vector

🧠 Use Case:

• Search
• Recommendation systems
• Semantic similarity
• Retrieval-augmented generation (RAG)

A vector DB

stores and indexes embeddings.

• Enables fast similarity search: "Find the top-5 documents most similar to this query."
• Often used in RAG systems to retrieve context before prompting an LLM.

🧠 How LLMs Use Embeddings + Vector DB

Typical RAG pipeline:

1. Chunk + embed all documents
2. Store vectors in a vector DB
3. At query time:
   • Embed the query
   • Search for similar vectors
   • Return top results
4. Inject retrieved text into prompt → LLM

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG)

MAKER Framework

AI Theory - MAKER framework

ways to make chatbot agentic

This is a problem we are tackling at Snapshot. as of 2026-01-14 We are trying to build a solution with the 3rd option, let's see if it succeeds

Here are the options to consider:

1. RAG search should be the last option because it's not accurate by nature, it is a similarity search.

2. defined interfaces using the backend server

• high maintenance - for new interface you need to go to backend, define schemas, run migrations, perhaps add more code in ai-manager as well

3. Read replica of DB, readonly user, and give the agent free access to query this DB on the fly.

• low maintenance
• uncontrolled and highly experimental

Storing data generated by AI

We ask LLMs to generate JSON data. (Some LLMs have a parameter to set the output format to JSON) and store the output in a SQL relational DB.

Some data/summaries generated by AI we save in a single column of type JSON. This is useful for experimental outputs we need to iterate a lot over.

For the rest of the data we know the schema of and know that shouldn't change much in the future, keep using regular relational structure.