LLM App Engineering
SkillDocs & knowledgeMaster modern LLM application engineering patterns. Use when designing prompt chains, evaluating output quality, managing token limits, streaming responses, or integrating LLMs into full-stack applications. SECURITY: Always sanitize user input before passing to LLM prompts to prevent prompt injection.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the LLM App Engineering skill
What this skill tells your AI
The instructions your AI receives, as published by neverinfamous/memory-journal-mcp in skills/llm-app-engineering/SKILL.md and read by ahel’s review.
Core principles for building robust LLM applications.
Key Patterns
- Prompt Engineering: Use few-shot, chain-of-thought, and clear system instructions.
- Context Window Management: Do not blindly append history to prompts. Implement sliding windows, summarization hooks, or token-based pruning to keep inputs within context limits and ensure high recall for recent instructions.
- Token Management: Track usage, handle limits gracefully, and summarize context dynamically.
- Structured Outputs: Always force structured outputs (e.g., JSON) using schema-guided generation or strict function calling rather than relying on prompt engineering to "ask nicely for JSON".
- Streaming: Always stream responses for perceived performance improvements in UX.
- Evaluations (Evals): Build automated eval pipelines for your prompts to catch regressions. Do not rely solely on human vibe checks. Implement automated deterministic evals (e.g., regex matching, JSON validation) and LLM-as-a-judge evals for semantic quality.
Signals
- GitHub stars
- 20
- Forks
- 5
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
llm-app-engineering- Source
- github.com/neverinfamous/memory-journal-mcp