How do I ground AI in my company's data?
RAG, vector stores, and fine-tuning, plus the dataset work that feeds it and the evals that show your answers are grounded.
The path.
- RAG & Retrieval Retrieval-Augmented Generation (RAG) is the practice of fetching relevant text (or other data) at query time and injecting it into the model's context so the model can answer from information it wasn't trained on.
- Vector Databases & Memory A vector database stores embeddings and serves approximate nearest-neighbor (ANN) search, which finds the closest vectors to a query vector quickly using index structures like HNSW (graph-based, the most common) or IVF/IVF-PQ (cluster + quantize, memory-efficient at scale).
- Fine-Tuning & Post-Training Reach for the cheapest tool that works, in this order:
- Dataset Engineering Data quality, not model choice, is usually the ceiling on a fine-tune.
- LLMOps: Evals, Observability & Guardrails Shipping LLM features without evals and observability is flying blind: outputs are non-deterministic, quality is subjective, and regressions are silent.
Check yourself.
One question per step. Take it cold to find where to start, or after reading to see what stuck. Nobody's grading you.
Best so far:
Question 1 of 5
-
Why is hybrid (BM25 + vector) the production default rather than vector search alone?
BM25 nails exact identifiers, names, and rare terms but is blind to paraphrase, while vector search captures meaning but misses exact matches. Fusing both covers each one's blind spots.
Covered in step 1 · RAG & Retrieval -
You already run Postgres and have a few million vectors. What's the chapter's advice?
At low millions of vectors, pgvector gives you SQL filtering, joins, and hybrid search without a second datastore to operate. Dedicated engines pay off only when scale or recall-at-latency SLOs push past Postgres.
Covered in step 2 · Vector Databases & Memory -
The model doesn't know your company's internal docs. Which lever does the chapter say to reach for?
The classic mistake is fine-tuning to add facts: it's an expensive, leaky way to do what RAG does better, and it bakes in staleness. Fine-tune for form and behavior, retrieve for knowledge.
Covered in step 3 · Fine-Tuning & Post-Training -
For instruction tuning, which dataset does the chapter say usually wins?
The LIMA-style "quality over quantity" finding has held up: a few thousand diverse, correct examples routinely beat hundreds of thousands of noisy ones. Curate for coverage and correctness, then stop.
Covered in step 4 · Dataset Engineering -
How do you check that your RAG feature's answers actually come from the retrieved context?
Output guards include groundedness/hallucination checks (does the answer match the retrieved context?), and RAG-specific evals like Ragas score faithfulness and context precision/recall.
Covered in step 5 · LLMOps: Evals, Observability & Guardrails