#rag
22 posts tagged with #rag
Every article below is hand-written, technically reviewed, and focused on rag. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning How to Evaluate EmbeddingGemma 2 for Multilingual RAG [2026]
EmbeddingGemma 2 is shiny. Shipping it into a multilingual RAG stack without regressions is not. Here’s an embeddings-only eval + rollout playbook that works.
Developer Tools How to Follow arXiv Updated Rate Limit Policy [2026]
arXiv tightened rate limits on Oct 1, 2026. Here’s a compliant ingestion blueprint for RAG: incremental harvest, caching, backoff, bulk S3, WARC snapshots, and version tracking.
AI and Machine Learning How to Do Synthetic Data for RAG Evaluation [2026 Pipeline]
A leakage-first, adversarial-negative pipeline to generate, label, and validate a synthetic RAG evaluation set without building a benchmark that only tests your generator.
AI and Machine Learning Jev Models Explained [2026]: Faster Routing, Reranking, JSON
Jev-style decision models are non-autoregressive models for routing, reranking, and structured outputs. Here’s how they work, where they fail, and how to evaluate them like a builder.
Developer Tools LLM Knowledge Base GitHub Template [2026]: Starter Kit Repo
Ship a forkable LLM knowledge base starter kit: a clean repo layout, ingestion scripts, GitHub Actions freshness checks, agent-readable templates, and retrieval evals that don’t rot.
AI and Machine Learning RAG Evaluation Metrics for Retrieval Quality: My Production Playbook
If your RAG app got worse after an embeddings or chunking change, grading answers won’t tell you why. Here’s a retrieval-first eval workflow: recall@k, MRR, citation accuracy, leakage tests, and a frozen offline corpus so regressions are real—not vibes.
AI and Machine Learning LLM Knowledge Base Architecture Guide [2026]: Wiki vs Notes vs RAG
A practical, vendor-neutral decision guide for wiki-style, notes-first, and RAG-first knowledge bases. Storage formats, chunking, ACL patterns, hybrid search, and a phased migration plan for agent-ready docs.
Cybersecurity How to Implement Field-Level Redaction for RAG Pipelines [2026]
An auditor-friendly RAG privacy pattern: deterministic redaction, optional reversible tokenization, TTLs, deletion propagation, and a leakage test suite in CI.
Developer Tools How to Upgrade Your LLM Wiki in 2026: Sync, Search, Agent-Readable
The 2026 LLM wiki problem isn’t setup. It’s keeping it synced across devices, searchable at scale (BM25 + embeddings), and safe for agents to ingest without prompt injection.
AI and Machine Learning RAG Data Leakage Test Suite [2026]: CI Red-Team Setup
Build an automated red-team suite for RAG apps: canary tokens, regex + similarity detectors, multi-step prompt-injection attacks, and a CI risk score that blocks risky merges.
Cloud and DevOps How to Pick LLM Application Observability Metrics [2026]
Token logs are table stakes. Here’s the minimum set of LLM application observability metrics for tools, RAG, caching, refusals, and privacy-safe logging that actually debugs production incidents.
AI and Machine Learning Data Privacy in RAG Redaction and Retention [2026 Playbook]
A practical engineering playbook for data privacy in RAG: what to redact, where privacy leaks happen, default retention periods, and audit trails you can defend in an audit without wrecking debugging.
Cybersecurity Prevent Sensitive Data Leakage in RAG: The 2026 Playbook
RAG leaks rarely happen in the model. They happen in logs, traces, and vector stores. Here’s a practical 2026 playbook to ship redaction, least-context retrieval, and auditable controls end-to-end.
AI and Machine Learning RAG Context Window Limits: Why Bigger Is Not Better [2026]
Expanding your context window from 4K to 128K tokens doesn't fix RAG — it masks retrieval failures with coherent-sounding hallucinations. Here's the measurement framework that actually works.
AI and Machine Learning Weaviate vs Chroma 2026: Production Power or Local-First Speed?
I'd pick Weaviate for any production RAG system serving more than a handful of users, and Chroma for rapid local prototyping where zero-config setup matters more than scale. The fault line isn't features — it's operational maturity versus developer ergonomics.
AI and Machine Learning Fine-Tuning vs RAG vs Prompt Engineering: Decision Framework [2026]
Stop guessing which LLM technique to use. A 2026-updated decision matrix with real cost figures, concrete examples, and a clear flowchart for when fine-tuning beats RAG, when RAG beats both, and when prompt engineering alone is the right answer.
AI and Machine Learning pgvector vs Pinecone 2026: Which Vector DB Should You Actually Use?
pgvector wins for teams already on Postgres who want simplicity and cost control; Pinecone wins for production AI apps that need managed, millisecond-scale vector search at massive scale. Your infrastructure context is the deciding factor.
AI and Machine Learning Milvus vs Qdrant 2026: Which Vector DB Wins for Production RAG?
Qdrant wins for lean, fast RAG deployments where simplicity and filtering speed matter most; Milvus wins for large-scale enterprise workloads demanding billion-vector search and deep ecosystem integrations. Your stack size and ops maturity should make this an easy call.
AI and Machine Learning Pinecone vs Weaviate 2026: Which Vector DB Actually Wins?
Pinecone wins for teams that need zero-ops managed infrastructure and fast time-to-production. Weaviate wins for teams that want open-source flexibility, hybrid search, and full data sovereignty.
AI and Machine Learning LangChain vs LlamaIndex 2026: Which LLM Framework Should You Pick?
LangChain wins for building complex, multi-step AI agents and conversational workflows; LlamaIndex wins for production-grade RAG pipelines and data-heavy retrieval systems. Choose based on whether your app is agent-first or retrieval-first.
AI and Machine Learning Qdrant vs Chroma 2026: Which Open-Source Vector DB Wins for RAG?
Qdrant wins for production RAG at scale; Chroma wins for local prototyping and developer speed. Here's the full breakdown to help you choose the right vector database before you're locked in.
Technology LLM Wiki Setup: Karpathy's Knowledge Base [2026 Guide]
I've been running Karpathy's LLM Wiki pattern for three months. Here's the real setup process, which agents work best, and where the pattern breaks down.
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