Best Practices for Building LLM Apps

What this best practices guide is

Vendor-neutral best practices for building LLM apps and agents, checked against the OWASP Top 10 for LLM Applications 2026, the OWASP Agentic Top 10, NIST's Generative AI Profile and official provider docs. It covers prompt design, evals, prompt injection, agent permissions, cost, observability, failure handling and data privacy.

It has 7 chapters and 31 practices, each with why it matters, what to do and what to avoid. For developers building with LLM APIs.

Chapter by chapter

1. Prompt design and structured output (4 practices)

Clear prompts and machine-readable output are the foundation. These practices make model behaviour predictable enough to test and to parse.

2. Evals before shipping (5 practices)

Evals are automated tests for model behaviour. Without them, every prompt or model change is a guess you only check in production.

3. Prompt injection and the OWASP LLM Top 10 (5 practices)

Prompt injection is LLM01 in the OWASP Top 10 for LLM Applications 2026. You cannot fully prevent it with prompts, so design the app so a fooled model cannot do much harm.

4. Tools, agents and least privilege (5 practices)

Excessive agency rose to LLM03 in 2026. These practices keep an agent's power proportional to what the task really needs.

5. Cost and latency (4 practices)

Token costs and response times decide whether a feature is viable. Most savings come from model choice, caching and limits.

6. Observability and data privacy (4 practices)

You need enough logging to debug and evaluate, and enough restraint to protect the people whose data passes through. NIST's Generative AI Profile lists data privacy among the risks generative AI makes worse.

7. Failures, fallbacks and misinformation (4 practices)

Model APIs time out, rate limit and occasionally return nonsense. Plan for each case so users get a clear answer instead of a spinner or a confident mistake.

How it works on Sefism

The guide reads like a short course: a syllabus, then one chapter per page in a focused reader, with a quiz at the end of each chapter for members. Every chapter is open to everyone.

Every claim is checked against the sources listed on the page (7 cited).