What can this model answer?

This assistant answers only from a local knowledge base of curated PDFs about Retrieval-Augmented Generation (RAG) and AI agents. Below is a plain-English map of that knowledge, plus real example questions — not generic "what is RAG" prompts, but the specific, use-case-style questions the model is built to handle.

RAG Vector Databases Agentic RAG Agent Memory MCP Multi-Agent Orchestration Production RAG

1 · RAG fundamentals & when to use it

How retrieval-augmented generation works, where it beats a plain LLM, and the failure modes to watch for.

When should I use RAG instead of fine-tuning a model on my own docs, and what are the trade-offs?

Walk through a real production example: how would KARLA-style retrieval be wired into a support bot, and where does it get used?

What are the most common reasons a RAG answer is confidently wrong, and how do I diagnose each one?

2 · Vector databases & retrieval internals

Embeddings, similarity search, chunking strategy, and how vectorless / page-index retrieval compares.

What chunk size and overlap give the best retrieval for long technical PDFs, and why?

Compare vectorless RAG with page-index retrieval against classic vector search — when is each the right call?

How does TurboVec fit into a RAG pipeline, and what latency/accuracy trade-offs does it introduce?

3 · Agentic RAG, memory & MCP

Letting an agent decide when to retrieve, how to remember across turns, and how tools (MCP) extend it.

Design a memory architecture for an agent that must recall facts across a 50-turn conversation without blowing up the context window.

What is MCP and how would I let my RAG agent call an external tool or database through it safely?

In agentic RAG, how does the agent decide whether to retrieve at all, and how do I stop it from over-fetching?

4 · Shipping it to production

Cost, latency, evaluation, security, and the architecture blueprints for a real deployment.

Give me an architecture blueprint for a production RAG service handling 10k questions/day on a single GPU.

What security risks show up when agents can read private docs, and which guardrails actually matter?

How do I evaluate whether my RAG system is good enough before launching — what metrics and tests?

Click any question above to jump straight into the chat, or hit Download Guide (top-left) to keep this map offline.

Start Chatting →