Prompt Engineering

Advanced prompt design for LLMs and autonomous agents. Covers reasoning patterns, template systems, optimization workflows, agentic orchestration, extended thinking, and tool use prompting.

When to use: Designing prompts that require structured reasoning, building agent loops, optimizing LLM output quality, creating reusable prompt templates, configuring extended thinking for complex tasks, or designing multimodal prompts with images and text.

When NOT to use: Simple factual queries, direct lookups, or creative writing that benefits from open-ended generation.

Key Principles

1. Explicit over implicit -- Modern models (Claude 4.x, GPT-4.1) follow instructions literally. Be specific about desired output, format, and behavior rather than relying on the model to infer intent.
2. Objective over instruction -- For reasoning models (OpenAI o-series, Claude with extended thinking), state the goal rather than prescribing step-by-step methods. These models plan natively.
3. Structure signals intent -- Use XML tags, clear delimiters, and consistent formatting to communicate prompt structure. Models trained on structured prompts parse them more reliably than plain text.
4. One good example beats many rules -- Few-shot examples with consistent formatting anchor model behavior more effectively than verbose instructions.
5. Feedback loops are built-in -- Design prompts that ask the model to verify, critique, or score its own output before finalizing.
6. Token economy matters -- Every extra token adds latency and cost. Compress context, remove filler, and front-load critical information.

Model-Specific Considerations