AI Production Playbooks

Production RAG Architectures

• 🔥 AWS Bedrock Patterns: Enterprise RAG systems on AWS Bedrock deliver production knowledge agents in 90 days with...

Postmortem reveals harsh prod realities for AI agents:

• Benchmark-style approach used to evaluate production AI agent.
• Unexpected failures hit hard, defying expectations.
• Most issues beyond model quality, exposing eval gaps.

Key trends in production RAG ramps:

• KRAG (Open-Source Private): 100% self-hosted multimodal platform using Modal GPUs, Florence-2 vision,...

Key patterns in scalable RAG architectures:

• Embeddings foundation: RAG memory built on numerical text representations.
• Ingestion pipeline (the...

Essential gates for agentic systems in production:

• Task success rate: Measures core effectiveness
• Tool call errors & containment rate: Ensure...

Rising trend in production RAG: high-ROI fixes via optimized retrieval to eliminate hallucinations.

• Hybrid search (BM25 + vectors) and re-ranking...

Only 7% of enterprises have AI-ready data. A semantic layer built on knowledge graphs pushes accuracy from 80% to 95%+, ideal for production in messy enterprise environments.

Evolving RAG trend: From one-shot retrieval to reasoning loops via LangGraph's stateful workflows.

• Naive RAG for basic QA over docs
  -...

MongoDB integrates vector search, automated embeddings, and reranking directly into its database, slashing AI app complexity and costs—a game-changer for production RAG pipelines.

RAG Production Failures and Fixes

• 🔥 Why Most RAG Systems Fail in Production: Most RAG systems work perfectly in demos and fail silently in...

Unlock 90% cost reductions on Bedrock with prompt caching—90% cheaper cached input, >80% latency drop.

Key production levers:

• Layered architecture:...

• Ingestion foundation: Parse, chunk, and embed documents before vector DB indexing
• Retrieval upgrade: Add layers to pipelines, combining methods...

Key insights from Dr. Sreedath Panat's lecture on scalable AI memory:

• Short-term (context window, per-call) vs long-term (external, persistent);...

Abhinav Kasliwal, Amazon AI leader, shares how to 3x accelerate GenAI prototypes to production for millions of users:

• 5 Pillars: Design,...

Key architectural choices for production RAG systems on AWS:

• Vector storage: Essential real-world decision beyond basic chatbots
• Chunking strategy: Core pattern for operational RAG deployment
  Dive into these for shipping cost-effective AI products.

Top AI products thrive by stopping guesses and starting rigorous evaluations of agents and products—this is the author's most comprehensive deep-dive yet.

Franz's AllegroGraph 8.5 delivers a semantic foundation for agentic AI, blending knowledge graphs, vector embeddings, and neuro-symbolic reasoning for...

Every FastAPI AI service hides a concurrency mistake, striking production inference architectures that externalise everything—the pattern most AI services, including RAG projects, converge on.

• Silent prod killers: RAG excels in demos but fails silently in production, despite fine retrieval, 200 OK APIs, and fluent model responses.
  -...

Real-world municipal AI war story transforming Town of Vail's microfiche/handwritten records from 1940s into actionable insights.

• Core stack:...