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RAG Engineering / 10Secure and Operate a RAG System
Carry authorization through retrieval, context, caches, and citations. Define trust boundaries, revocation behavior, and operational controls.
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Blog posts in the AI ENGINEERING category
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RAG Engineering / 09Evaluate Retrieval and Gate Regressions
Separate retrieval coverage, context quality, and answer support. Calculate ranking metrics and design reproducible regression gates.
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RAG Engineering / 08Grounded Answers, Citation Validation, and Failure Recovery
Separate schema validity, citation identity, and factual support; design bounded repair, honest abstention, and streaming behavior around those distinct guarantees.
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RAG Engineering / 07Query Planning and Safe Structured Retrieval
Translate questions into bounded retrieval plans, preserve metadata constraints, and verify SQL result semantics with a worked incident-table example.
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RAG Engineering / 06Hybrid Search, RRF, and Bounded Corrective Retrieval
Work through reciprocal rank fusion, distinguish full-text search from BM25, and evaluate reranking and corrective retrieval without treating scores as confidence.
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RAG Engineering / 05Context Assembly, Parent Retrieval, and Multimodal Evidence
Turn retrieved candidates into useful context by preserving qualifiers, tracking expansion provenance, budgeting tokens, and separating image retrieval from visual answering.
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RAG Engineering / 04Embedding Models and Vector Index Compatibility
Understand vector geometry, pin the full encoding contract, and evaluate approximate search under real filters before migrating an embedding index.
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RAG Engineering / 03Versioned Ingestion and Safe Index Publication
Design document ingestion around immutable representations, idempotent jobs, and atomic publication so failed updates cannot silently replace searchable evidence.
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RAG Engineering / 02Chunking, Parsing, and the Evidence You Lose
Compare chunk boundaries on the same source, preserve qualifiers and table structure, and evaluate retrieval against stable evidence rather than arbitrary chunk IDs.
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RAG Engineering / 01How RAG Works, from Documents to Answers
Understand how RAG connects documents to an LLM: build an index, retrieve passages, assemble context, and return an answer with traceable sources.
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RAG in 2026: From Vector Search Demo to Reliable Evidence System
A system-level guide to the twelve classic RAG failure modes, the techniques now used to address them, and the limits that still remain.
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Standing on Open Source: How a Codex Edge Case Became Agentusage
How two OpenUsage projects inspired me to investigate a Codex JSONL edge case and build a local-first dashboard for AI coding agents.
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Use Local Models in VS Code Copilot with LM Studio and Unsloth Studio
How to connect VS Code Copilot Chat to local OpenAI-compatible models served by LM Studio or Unsloth Studio, including the BYOK utility-model setting that prevents agent errors.
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KV-Centric LLM Serving: vLLM, SGLang, and Disaggregated Attention
A practical mental model for KV cache memory, vLLM PagedAttention, SGLang RadixAttention, and prefill/decode disaggregation in modern LLM serving.
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Reproducing vLLM and LMCache KV Cache Reuse on a CPU-Only MacBook
A reproducible CPU-only vLLM and LMCache workspace for understanding KV cache reuse, transfer modes, and local inference debugging on Apple Silicon.
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Use Databricks Models with VS Code Copilot and Copilot CLI
How to use one local proxy to connect Databricks Model Serving to both VS Code Copilot Custom Endpoint and GitHub Copilot CLI.
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A Git-Native Message Channel for Local Coding Agents
How I use a Git ref as a local message channel between Claude Code and Codex for implementation, independent review, and iterative bug fixing.
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AI Terminology: Agents, Skills, RAG, MCP, and the Layers Beneath the Hype
A practical mental model for AI terminology: prompt, context, memory, agents, RAG, function calling, MCP, workflows, skills, and sub-agents.
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The Mandate for Leadership in AI Engineering
Over the next 12 to 24 months, the differentiator among engineers will shift from mastery of programming languages like Rust, Go, or Python, or the volume of code produced, to the...
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LangGraph Reflection
Reflection is related to agent self-improvement or reasoning feedback loops.
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LangGraph Sample Project
A LangGraph sample project with separate research, analysis, and supervisor services, REST communication, and retry handling.
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LangChain/LangGraph Q&A
LangGraph’s core philosophy is to orchestrate multi-step, stateful agent workflows using a directed graph (DAG or DG) structure. Each step is modeled as a node, while data and...
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FastMCP MCP Server Hub
Currently, our different projects are using various MCP servers. To streamline and unify the process, we plan to implement a HUB MCP server that can handle multiple MCP Servers and...
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LangChain Retry Logic
LLM call is not stable and may fail due to network issues or other reasons, therefore, retry logic is necessary.
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MCP Transports
Compare stdio, SSE, and Streamable HTTP transports for MCP, with Python examples for configuring servers and clients.
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Text to SQL (Smolagents)
A smolagents text-to-SQL example with query execution logs, answers from a receipts database, and tracing with Phoenix.
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MCP Server & Client (SSE)
Beside the vscode extension hosts like (cline, continue), you can write your own client to connect to the MCP server. The client should be able to send and receive messages using...
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RAG-Reranking
Retrieval-Augmented Generation (RAG) is a powerful approach that combines retrieval and generation to produce high-quality responses. However, the quality of the final response can...
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GenAI Projects
A collection of generative AI resources and project notes on schema mapping, feature engineering, PDF extraction, and agent frameworks.
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Crawling the Web with LLM
Web crawling frameworks and a ScrapeGraphAI example that uses local Ollama models to extract structured information from a webpage.
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LangGraph VS AutoGen
Compare LangGraph and AutoGen across workflow structure, state management, customization, and use cases for LLM applications.
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Autogen Intro and RAG Workflow
AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans.
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Gradio with Ollama
Notes on processing unstructured PDF content and a demo architecture connecting a Gradio interface to models served by Ollama.
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AutoGen HttpClient
Now we can add our proxy config in our client, which will resolve connection issue.