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#rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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6 Python Audit Tests for PDF Answers: Changelog Tracker Citation Tracing

6 Python Audit Tests for PDF Answers: Changelog Tracker Citation Tracing

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5 min read
When a Gaming Digest Web Search API Returns Nothing for Niche Updates

When a Gaming Digest Web Search API Returns Nothing for Niche Updates

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6 min read
Fintech News Service Retrieval: Auditable Evidence Without Unlimited Retention

Fintech News Service Retrieval: Auditable Evidence Without Unlimited Retention

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8 min read
RAGFlow 0.26.4 in Production: Strong Parsing, Expensive Baseline, Unproven Tenant Isolation

RAGFlow 0.26.4 in Production: Strong Parsing, Expensive Baseline, Unproven Tenant Isolation

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11 min read
Node.js Candidate Retrieval: Three Tenant-Isolation Gates for Game Hiring

Node.js Candidate Retrieval: Three Tenant-Isolation Gates for Game Hiring

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6 min read
PDF Knowledge Retrieval Under Latency Budgets — An Architecture Decision Record

PDF Knowledge Retrieval Under Latency Budgets — An Architecture Decision Record

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5 min read
Grounded answers: how Graphify tags what it knows

Grounded answers: how Graphify tags what it knows

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3 min read
Recruiting Candidate Search Source Deduplication — A 3-Stage Node.js Retrieval Design

Recruiting Candidate Search Source Deduplication — A 3-Stage Node.js Retrieval Design

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5 min read
Financial Research Dashboard Retrieval: Privacy Controls and Freshness Explained

Financial Research Dashboard Retrieval: Privacy Controls and Freshness Explained

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5 min read
Travel Itinerary Retrieval Architecture: Versioning for Grounded Citations

Travel Itinerary Retrieval Architecture: Versioning for Grounded Citations

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5 min read
The Best Way to Structure Data for AI Agents

The Best Way to Structure Data for AI Agents

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10 min read
pg_vectorsearch Claims 6 ms at 100M Vectors. Filtered Queries Can Come Back Short

pg_vectorsearch Claims 6 ms at 100M Vectors. Filtered Queries Can Come Back Short

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7 min read
Get YouTube transcripts for every new upload from the channels you follow (for RAG, summaries and research)

Get YouTube transcripts for every new upload from the channels you follow (for RAG, summaries and research)

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3 min read
The Answer Was in the Middle of the Context and the Model Walked Past It

The Answer Was in the Middle of the Context and the Model Walked Past It

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2 min read
Designing RAG for Lawyers: Refusal Is a Feature

Designing RAG for Lawyers: Refusal Is a Feature

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2 min read
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