A production-oriented prompt engineering series, covering prompt structure, context design, few-shot examples, chain-of-thought patterns, prompt injection defense, Prompt CI/CD, version management, A/B testing, automated regression checks, and LLM-as-a-Judge evaluation.
Learn prompt engineering with zero-shot, few-shot, Chain-of-Thought, and ReAct patterns. Build structured prompts, evaluate them on held-out tasks, and add practical safety boundaries for production LLM applications.
Build prompt-injection-resistant LLM applications with explicit trust boundaries, least privilege, deterministic tool authorization, provenance-aware data flow, egress controls, bound user confirmations, sandboxing, and adaptive security evaluations. Covers direct, indirect, RAG, memory, multimodal, MCP, and persistent attacks.
A practical, provider-neutral guide to context engineering for LLM and Agent systems. Design a context contract, select and retrieve evidence, compress without losing meaning, persist state with provenance and deletion, budget tokens and latency, defend against untrusted content, and evaluate context changes with task-level evidence.
A hands-on companion to context engineering for coding and Agent workflows. Build a bounded task packet, select versioned evidence, maintain durable decisions without treating memory as authority, compress with source links, measure retrieval and cache behavior, and verify permissions, privacy, quality, latency, cost, and rollback.
A practical introduction to prompt injection guardrails for LLM applications: input signals, trusted-data boundaries, least-privilege tools, deterministic authorization, output controls, and security testing.
LLM hallucinations occur when AI generates plausible but false information. Learn detection methods, RAG strategies, and prompt techniques to build reliable AI apps.
Understand tokenization, context-window budgets, and long-context failure modes without relying on stale model tables or character-per-token rules. This guide explains tokenizer boundaries, input/output reservations, safe truncation, cost reconciliation, chunking, caching, multilingual measurement, and task-level evaluation.
Build reliable LLM tool-calling systems with strict schemas, deterministic dispatch, real-user authorization, idempotency, bounded loops, timeout and retry policy, safe parallelism, untrusted tool-result handling, observability, and evaluation. Explains OpenAI, Anthropic, structured outputs, and MCP boundaries.
A version-aware guide to team rules and prompt templates for Cursor. Learn how to separate project conventions from security policy, review generated changes, version scenario prompts, measure failure modes, and evolve shared AI-assisted development guidance without treating instructions as a guarantee.
Build reliable Cursor and TRAE coding workflows with version-aware rules, explicit file scope, reviewable plans, tests, and rollback. This guide separates provider features from general Prompt practice and avoids unsupported success-rate claims or hard tool promotion.
Use chain-of-thought prompting without treating visible explanations as hidden model reasoning. Compare direct answers, decomposition, few-shot CoT, self-consistency, verifiers, and Tree of Thoughts; then choose a strategy by model, task, accuracy, latency, privacy, and evaluation requirements.
Understand the 'Lost in the Middle' problem in long-context LLMs. Learn why models with 1M+ token windows forget information in the middle of prompts and how to mitigate this using advanced Context Engineering.
Design a versioned context architecture for AI coding workflows. This guide separates provider-specific rule files, reusable prompts, MCP tools, retrieval, evaluation, security, precedence, and token budgets without treating any filename as a universal standard.
A practical guide to writing AGENTS.md-style context contracts for coding Agents. Separate project guidance from trusted policy and secrets, pin supported tool behavior, define scoped tasks and evidence, defend against instruction injection, and require tests, review, least-privilege tools, and reversible changes instead of trusting natural-language instructions.
A practical guide to governing instruction files, prompt templates, and Agent profiles for AI coding. Pin host-tool behavior, distinguish untrusted context from trusted policy, define scope and evidence, defend against injection, use least-privilege tools, and validate changes through tests, review, and reversible rollout.
A comprehensive engineering guide to Prompt CI/CD practices, covering Git-based version control, A/B testing framework design, LLM-as-Judge automated regression detection, and integration with LangSmith/Braintrust platforms. Includes complete Python code examples and pipeline architecture diagrams.