Technical learning document
When retrieval goes wrong, models still sound confident. These moments show failure modes practitioners warn about — and what to listen for when you evaluate answers.
Curated follow-ups for RAG — open another explanation, then save what matters.
Save expert explanations into one investigation, compare voices, and export a shareable research brief on this device.
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Chosen for clarity and how directly it answers the question — not for views or hype.
"You might be missing data. You might be chunking them in the wrong way. You might be using an embedding model that isn't optimum. Maybe your retrieval strategy needs to change."
Webinar: Fix Hallucinations in RAG Systems with Pinecone and Galileo · Pinecone · 19:48
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Supporting clips that deepen the guide's theme.
Matt Gotteiner: We've got Java and JavaScript SDK.
Why this is worth watching: Worth hearing after the opening clip.
Opens a little earlier so you catch the setup
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How practitioners frame real design choices — not a single “right” answer.
Over-constraining citations can make answers brittle; under-constraining invites hallucination.
Bad chunks or weak recall can still produce plausible but wrong answers.
Different experts and framings on the same topic — compare before you decide.
"a failure of OpenAI's training—where they have the intentions and they haven't met them yet— versus what is something th"
State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490
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"models we have regarding the embedding and how to select the best"
LLM Fine-Tuning Course – From Supervised FT to RLHF, LoRA, and Multimodal
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"vector search is that we can take some input"
Agentic RAG: build a reasoning retrieval engine with Azure AI Search | BRK142
Tutorial / walkthrough style
"you actually optimize your rag"
Building Production-Ready RAG Applications: Jerry Liu
Technical / systems framing
Referenced by multiple experts — 4 distinct channels in this comparison.
OCR noise and bad splits are the most common root cause of confident wrong answers.
From retrieval basics through indexes, reranking, and evaluation.
Contrasting explanations from long-form talks — use both sides to stress-test your design, not to pick a winner.
Teams disagree on when to retrieve context versus adapt model weights.
Keep weights frozen; ground answers with external chunks.
Adapt the model when domain vocabulary is fixed and labeled data exists.
Chunk size and overlap change recall and answer quality in different ways.
Higher recall for precise facts; more index noise.
Better narrative context; risk missing needle facts.
Some engineers ship hybrid search; others rely on dedicated vector stores.
Scale ANN indexes and metadata filters separately.
Combine BM25 with embeddings in one stack.
Sequenced RAG engineering path — each link builds on the last concept, not random suggestions.
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