Blog
Field notes from building AI systems that survive contact with users.
No trend pieces. Just the patterns, measurements, and failure modes we run into while shipping prompt systems and content pipelines.
Designing Streaming LLM Responses That Feel Instant
Streaming is not just about speed — it is about perceived performance. How to chunk, display, and handle partial tool calls in a streaming UI.
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Prompt Injection Defense: What Actually Works in 2026
Prompt injection is the SQL injection of the LLM era. Practical defenses that do not rely on hoping the model ignores malicious input.
Read articleContext Engineering Is the New Prompt Engineering
Prompts are a fraction of what the model sees. The real lever is everything in the context window — and most teams never audit it.
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Evaluating Autonomous Agents: Beyond Single-Turn Metrics
Single-turn evals do not capture agent behavior. How to measure success rate, cost-per-success, and safety for multi-step agent loops.
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RAG Pipeline Architecture: From Naive to Production
A naive RAG pipeline retrieves and stuffs. A production pipeline chunks, reranks, grounds, and guards. Here is the difference.
Read articleAI Trends Worth Your Attention
A filter for the release cycle: which shifts actually change how you build, and which ones you can safely ignore this quarter.
Read articleLLM Cost Optimization: Cutting Spend Without Cutting Quality
Most teams overpay for LLM inference by 40-60%. Model routing, prompt compression, and caching are the levers that actually move the needle.
Read articleContext Window Management: What to Keep, What to Evict
A full context window is not a feature — it is a liability. Strategies for prioritizing, compressing, and evicting context in long agent runs.
Read articleRetrieval Reranking That Actually Works
Vector search gets you candidates. Reranking decides which ones the model sees. Here is how to build a rerank step that earns its latency.
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AI Content Strategy in 2026: Beyond SEO-Flavored Text
Flooding your blog with AI-generated articles is not a strategy. Here is how to use AI in content without losing the trust that earns rankings.
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Structured Output Engineering: JSON, Not Prose
When you need reliable, parseable output, you need structured generation. How to enforce schemas and stop fighting the model over formatting.
Read articleAgent Memory Systems: Beyond Stuffing Everything Into Context
Long-lived agents need memory that survives across sessions. Working memory, episodic memory, and semantic memory — and when each fails.
Read articleModel Routing Patterns: Sending Requests to the Right Model
A single model for everything is expensive and slow. A routing layer that picks the model per request is the architecture that scales.
Read articleAgent Loops That Don't Spiral
Most agent failures are loop-design failures. Here is how to bound an agentic run so it finishes, reports, and stays cheap.
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Eval-Driven Development for Prompts
If you change a prompt without a test set, you are debugging in production. Here is how to build a prompt eval loop you will actually run.
Read articleAI Trends to Watch in Late 2026: Agents, Evals, and Edge
Three shifts are reshaping how teams build with LLMs: autonomous agents going production, eval-driven development maturing, and edge inference.
Read articlePrompt Versioning and CI/CD: Treating Prompts Like Code
Prompts change more often than application code. Without versioning, testing, and rollback, every change is a gamble. Here is the system.
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Building With MCP: Tool Standards for Agent Interoperability
The Model Context Protocol is becoming the standard for how agents discover and call tools. What it changes and what it does not.
Read articleHallucination Reduction: Techniques That Actually Work
You cannot eliminate hallucination, but you can reduce it dramatically. Grounding, verification, and canaries — the techniques that survive production.
Read articleDesigning Tools Agents Actually Want to Use
MCP made tool plumbing standard. Good tool design is still rare — and it is the difference between an agent that finishes and one that flails.
Read articleAgent Observability: Logging, Tracing, and Alerting in Practice
An agent you cannot observe is an agent you cannot debug. What to log, how to trace, and which alerts wake you up at 3am.
Read articlePrompt Testing Strategies: From Vibe Checks to Regression Suites
Testing prompts by reading the output is not testing. How to build labeled sets, scoring rubrics, and regression suites that catch regressions.
Read articleBuilding an AI Video Content Pipeline for YouTube
From idea to published video: how to use AI at each stage without letting it flatten your voice. A practical pipeline for technical creators.
Read articlePractical Patterns for Agent Memory
Stateless agents forget the user every turn. Real agents need memory — but the wrong kind makes them worse. Four patterns that hold up.
Read articleDesigning Tools an Agent Can Actually Use
Agents rarely fail because they cannot reason. They fail because the tools you handed them are ambiguous, chatty, or unsafe.
Read articleRunning a Hallucination Audit on Your Prompts
You cannot eliminate hallucination. You can find where your prompts invite it and wall those spots off. A repeatable audit process.
Read articleModel Routing Without Over-Engineering It
Not every task needs the strongest model. A simple routing rule beats a clever one you never maintain. How to split traffic by difficulty.
Read articleContext Engineering Beats Longer Prompts
Bigger context windows made prompt bloat cheap and quality worse. What matters is what you put in the window, and in what order.
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Observability for Agents: Logging the Right Thing
Agents fail in ways single calls cannot. To debug them you need traces of decisions, not just inputs and outputs. What to log.
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Chain-of-Thought Prompting Without the Hand-Waving
Reasoning steps are not magic words. Here is when step-by-step prompting actually improves output quality, and when it just burns tokens.
Read articleWriting System Prompts That Hold Up in Production
A system prompt is a contract, not a personality quiz. A layered structure that survives edge cases, model swaps, and six months of feature creep.
Read articleRAG Basics That Actually Move the Needle
Retrieval quality sets the ceiling on answer quality. Chunking, hybrid search, and the reranking step most teams skip.
Read articleEvaluating AI Output Without Fooling Yourself
Vibe checks scale to about ten examples. Build a small eval harness in an afternoon and stop shipping regressions.
Read articleAn AI Content Workflow That Doesn't Read Like AI
The generic-draft problem is a process problem. A five-stage pipeline where the model does research and structure, and a human keeps the voice.
Read articleSeven Prompt Patterns Worth Reusing
Persona, rubric, few-shot, decomposition, self-critique, escape hatch, and format lock — with the failure each one repairs.
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