Let’s Demystify RAG, shall we?
RAG stands for Retrieval-Augmented Generation. Your AI sounds confident yet gets facts wrong. RAG fixes that by grounding decisions in your data, so they aren’t built on sand.
Here’s what you might not be aware of: every time you upload documents to ChatGPT, you’re already using a mini RAG system. No coding, no setup, no vector databases—just drag, drop, and query.
Let’s Go Back to The Technicalities :)
What it is: retrieve relevant documents first, then generate the answer using those "ingredients." Think open-book exam with citations.
When to use it: any workflow where accuracy and freshness matter—policy, customer support, legal, finance, ops dashboards.
Why it matters: fewer hallucinations, lower training costs vs. broad fine-tuni…
Let’s Demystify RAG, shall we?
RAG stands for Retrieval-Augmented Generation. Your AI sounds confident yet gets facts wrong. RAG fixes that by grounding decisions in your data, so they aren’t built on sand.
Here’s what you might not be aware of: every time you upload documents to ChatGPT, you’re already using a mini RAG system. No coding, no setup, no vector databases—just drag, drop, and query.
Let’s Go Back to The Technicalities :)
What it is: retrieve relevant documents first, then generate the answer using those "ingredients." Think open-book exam with citations.
When to use it: any workflow where accuracy and freshness matter—policy, customer support, legal, finance, ops dashboards.
Why it matters: fewer hallucinations, lower training costs vs. broad fine-tuning, instant updates as your knowledge changes.
3 Takeaways
Start small: list your top 10 questions, pick one, index only the docs that answer them (FAQs, SOPs, policies).
Make retrieval stronger: chunk cleanly, add metadata, use hybrid search (keywords + vectors), re-rank; log sources in every answer.
Measure reality: create "golden" Q&A sets; track faithfulness, latency, and resolution rate; improve what fails.
As I highlighted before, RAG is the simple discipline of giving models the right pages before they write. E.g., OpenAI highlighted how Navan uses file search to deliver precise travel-policy answers inside its agent—classic RAG in production.
AI and the New Database Landscape for LLM Applications
Ever wonder how your AI chatbot seems to "remember" facts or search your documents? It’s not magic — it’s the database. Today’s AI-powered applications depend on intelligent data retrieval and storage systems.
Limits & Fixes
Bad retrieval = bad answers. Fix with better chunking, domain-specific embeddings, reranking, and continuous eval sets. (See my notes on context and RAG’s role in "database + AI" design.)
Latency & cost. Retrieval adds hops. Cache popular answers, restrict scope, and pair with a smaller model for reranking before your main model. Keep a human in the loop for high-stakes outputs.
Beyond Prompts: How Context Engineering Is Shaping the Next Wave of AI
Imagine if building an AI was less about crafting "magic" prompts and more like directing a blockbuster film, where the script, sets, and context shape every outcome.
Your Move
This week, audit one customer-facing workflow. Ship a tiny RAG loop: 25 docs, 15 golden questions, source-grounded answers. If it reduces escalations or response edits, scale. Just start—one win beats waiting for flawless.
AI Tool: Wispr Flow
Wispr Flow is a voice-to-text AI tool that converts speech into polished written content across various applications. It aims to boost productivity for busy professionals by enabling faster content creation and task automation through natural language dictation. The tool highlights HIPAA-eligible security across all plans and SOC 2 Type II compliance for Enterprise plans, making it suitable for sensitive data handling in regulated industries.
Written by Dr. Hernani Costa and originally published at First AI Movers. Subscribe to the First AI Movers Newsletter for daily, no‑fluff AI business insights and practical automation playbooks for EU SME leaders. First AI Movers is part of Core Ventures.