Consider this query: Retrieve the ssns of employees who work on... | Filo
Consider this query: Retrieve the ssns of employees who work on... …
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Lesson 19: Introduction to Naïve RAG
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Lesson 19: Introduction to Naïve RAG
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Ganesh | AI Engineer on Instagram: "Traditional RAG is a straight pipeline: retrieve → generate → done If retrieval is wrong, the answer is wrong. Its literally the way you design it than adding any rocket science. To make RAG self-correcting, we use Agentic RAG — a feedback loop.👇 ⸻ 1️⃣ What Agentic RAG Actually Does Instead of trusting the first retrieved chunks, it: ➡ retrieves ➡ checks relevance ➡ validates the answer ➡ retries if needed It behaves like a system that knows when it’s wrong.
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Ganesh Jha on Instagram: "Here is what you’ll learn: RAG Fundamentals: context window limits, hallucinations; retrieval (find relevant chunks) + generation (LLM answers). ​ Prompt Engineering Base: PICF-ATD framework (Persona, Identity, Context, Format, Audience, Tone, Data) for all RAG/agentic pipelines. ​ Data Ingestion/Preprocessing: PyMuPDF for PDFs; Tesseract OCR; Docling—handles real parsing challenges skipped in short tutorials. ​ Chunking Strategies (Theory + Code): Fixed, semantic, stru
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