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Move beyond simple prompts to structured protocols. Learn how to engineer AI agents using the Bernstein methodology and TruLens observability for robust, testable LLM applications.
Treat LLM inputs like untrusted user data. Learn to detect and mitigate prompt injection attacks in AI agents using layered defense, input sanitization, and output validation.
Originally published at AI Agent Security: The Runtime Surface Around Every Tool Call on...
Discover how prompt injection mirrors SQL injection and learn defensive architectural patterns to secure LLM-powered applications in production.
You spend twenty minutes typing a prompt. You hit generate. And what comes out looks… fine. Not bad....
I treat prompt caching as FinOps: order the static prefix first, keep it byte-stable, and stop re-paying to re-read an 8K preamble on every request.
The first version of most AI features is not a system. It is one giant prompt doing too many...
The tenth prompt tweak usually feels like progress. The eleventh reveals the problem: the fix that...
The tenth prompt tweak usually feels like progress. The eleventh reveals the problem: the fix that...
A technical guide to the 2026 AI engineering stack, covering OpenAI observability, local-first agents, and code review integration.
From Prompt to Context to Harness: how three years of AI engineering vocabulary kept renaming the same problem until 40% of agent projects started failing for the same boring reason.
14 docs × 4 instruction conditions = 56 outputs from claude-opus-5. Median change −6.5 to −8.4%, 3/56 reached −20%, with or without constraints. All 18 price strings survived, count strings vanished, and the same acceptance-criteria phrase was cut in every condition.