Production LLM Systems Builder

The problem

Demos look great, but production LLM systems often fail due to hallucinations, unpredictable costs, latency spikes, and security vulnerabilities like prompt injection. Most developers struggle to move from a basic prompt to a resilient architecture that handles real-world data and user abuse.

What it does

• Designs RAG pipelines with semantic chunking, metadata filtering, and citation-backed generation.
• Implements agent orchestration using plan-execute-review loops and explicit tool contracts.
• Builds safety guardrails to detect prompt injection, redact PII, and enforce content policies.
• Configures observability for tracing model calls, token usage, and cost per outcome.
• Establishes evaluation frameworks using golden sets to measure accuracy and drift.

Frameworks & tools

Vector databases, LLM provider APIs (OpenAI, Anthropic, local models), Pydantic for structured outputs, and LLM observability platforms.

Why this beats prompting it yourself

This skill shifts focus from "writing better prompts" to building deterministic systems around probabilistic models. It enforces production standards like ACL-based retrieval filtering and human-in-the-loop approvals that a standard chat prompt skips.

Use cases

• Building enterprise chat assistants over sensitive internal document repositories.
• Automating multi-step workflows where agents must call APIs and databases safely.
• Hardening existing prototypes with structured output validation and fallback paths.
• Optimizing high-traffic LLM features for cost and latency through routing and caching.

Known limitations

Does not cover training models from scratch or general application frontend logic. Requires existing model access and deployment targets.