--- title: How do I ground AI in my company's data? — The Agentic AI Hub | daily.dev description: RAG, vector stores, and fine-tuning, plus the dataset work that feeds it and the evals that show your answers are grounded. canonical: https://daily.dev/agentic-ai-hub/paths/ground-in-your-data/ og:type: website og:url: https://daily.dev/agentic-ai-hub/paths/ground-in-your-data/ og:title: How do I ground AI in my company's data? — The Agentic AI Hub | daily.dev og:description: RAG, vector stores, and fine-tuning, plus the dataset work that feeds it and the evals that show your answers are grounded. og:image: https://daily.dev/assets/og/agentic-ai-hub.png?v=40e6ff69 og:site_name: daily.dev og:locale: en_US twitter:card: summary_large_image twitter:site: @dailydotdev twitter:creator: @dailydotdev twitter:title: How do I ground AI in my company's data? — The Agentic AI Hub | daily.dev twitter:description: RAG, vector stores, and fine-tuning, plus the dataset work that feeds it and the evals that show your answers are grounded. twitter:image: https://daily.dev/assets/og/agentic-ai-hub.png?v=40e6ff69 --- [Skip to main content ](#main-content) # 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. [Start reading → ](/agentic-ai-hub/rag-retrieval/?path=ground-in-your-data&i=0) [Check yourself first ↓ ](#quiz) 1. [ 1 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. › ](/agentic-ai-hub/rag-retrieval/?path=ground-in-your-data&i=0) 2. [ 2 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). › ](/agentic-ai-hub/vector-databases-memory/?path=ground-in-your-data&i=1) 3. [ 3 Fine-Tuning & Post-Training Reach for the cheapest tool that works, in this order: › ](/agentic-ai-hub/fine-tuning-post-training/?path=ground-in-your-data&i=2) 4. [ 4 Dataset Engineering Data quality, not model choice, is usually the ceiling on a fine-tune. › ](/agentic-ai-hub/dataset-engineering/?path=ground-in-your-data&i=3) 5. [ 5 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. › ](/agentic-ai-hub/llmops-evals-observability-guardrails/?path=ground-in-your-data&i=4) ## 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 1. Step 1 Why is hybrid (BM25 + vector) the production default rather than vector search alone? A BM25 pre-filters candidates so vector search can scale B Vector indexes are too expensive to run over full corpora C Lexical and semantic retrieval fail in different, complementary ways D Hybrid search removes the need for a reranker 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 → ](/agentic-ai-hub/rag-retrieval/?path=ground-in-your-data&i=0) Next question → 2. Step 2 You already run Postgres and have a few million vectors. What's the chapter's advice? A Use pgvector, keeping one system for vectors, metadata, and transactions B Adopt a managed vector DB now to avoid migrating later C Deploy Milvus, since real scale demands a distributed engine D Put vectors in Redis for latency and keep metadata in Postgres 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 → ](/agentic-ai-hub/vector-databases-memory/?path=ground-in-your-data&i=1) Next question → 3. Step 3 The model doesn't know your company's internal docs. Which lever does the chapter say to reach for? A Fine-tune so the knowledge lives in the weights B Pretrain a small domain model from scratch C Distill a frontier model onto your documents D RAG, since fine-tuning is for behavior and form, not injecting facts 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 → ](/agentic-ai-hub/fine-tuning-post-training/?path=ground-in-your-data&i=2) Next question → 4. Step 4 For instruction tuning, which dataset does the chapter say usually wins? A Hundreds of thousands of scraped examples for coverage B A few thousand diverse, high-quality curated examples C Fully synthetic data from the strongest frontier model D The largest open corpus you can legally use 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 → ](/agentic-ai-hub/dataset-engineering/?path=ground-in-your-data&i=3) Next question → 5. Step 5 How do you check that your RAG feature's answers actually come from the retrieved context? A Groundedness checks and RAG evals like Ragas B Lower the model temperature to zero C A bigger context window for more retrieved docs D Pairwise Elo ranking of responses 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 → ](/agentic-ai-hub/llmops-evals-observability-guardrails/?path=ground-in-your-data&i=4) See results → of 5 * [ 1 RAG & Retrieval → ](/agentic-ai-hub/rag-retrieval/?path=ground-in-your-data&i=0) * [ 2 Vector Databases & Memory → ](/agentic-ai-hub/vector-databases-memory/?path=ground-in-your-data&i=1) * [ 3 Fine-Tuning & Post-Training → ](/agentic-ai-hub/fine-tuning-post-training/?path=ground-in-your-data&i=2) * [ 4 Dataset Engineering → ](/agentic-ai-hub/dataset-engineering/?path=ground-in-your-data&i=3) * [ 5 LLMOps: Evals, Observability & Guardrails → ](/agentic-ai-hub/llmops-evals-observability-guardrails/?path=ground-in-your-data&i=4) Try again [Where to next → ](#path-next-title) ## Where to next. More guided paths through the handbook. Pick the next question worth answering. [ How do I build my own agent? → ](/agentic-ai-hub/paths/build-your-own-agent/)[ Can I trust what agents produce? → ](/agentic-ai-hub/paths/trust-agent-output/)[ How do I ship an AI feature to production? → ](/agentic-ai-hub/paths/ship-to-production/) [All reading paths → ](/agentic-ai-hub/#paths-start-here) [Browse every chapter ](/agentic-ai-hub/#pages) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/og-image.png?v=a830cdf1","width":1200,"height":630},"sameAs":["https://twitter.com/dailydotdev","https://www.linkedin.com/company/dailydotdev","https://github.com/dailydotdev","https://www.instagram.com/dailydotdev"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","description":"Free, personalized developer news aggregator. 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