LLM Integration & Prompt Engineering
Language models wired into production systems that actually do useful work.
I integrate large language models (OpenAI, Anthropic, local Llama via Ollama) into existing systems and workflows. This includes API integration, prompt design, context management, structured output parsing, and the reliability engineering needed to make LLM calls predictable in production.
Use Cases
Document classification and extraction
Feed invoices, contracts, or support tickets to a structured LLM call. Returns typed, validated fields ready for your database.
Content generation pipelines
Parameterised prompt templates that produce on-brand content at scale — product descriptions, email drafts, reports — with human review gates.
Conversational interfaces
Chat interfaces with full conversation history, system prompt management, and guardrails to keep responses on-topic.
Self-hosted LLM deployment
Run Llama 3 or Mistral behind a FastAPI gateway on your own infrastructure. Zero data leaves your server; cost per token approaches zero.
Common Questions
Which LLM should I use?
Depends on the task. GPT-4o for reasoning-heavy work; GPT-4o-mini or Claude Haiku for high-volume, cost-sensitive tasks; local Ollama for privacy-critical deployments.
How do you handle hallucinations?
Structured output schemas (JSON mode / Pydantic), grounding with retrieved context, confidence thresholds, and human-in-the-loop for sensitive actions.
What does a typical LLM integration cost to run?
Highly variable. A classification pipeline running 1000 calls/day costs ~$1–5/month on GPT-4o-mini. I always build in token counting and cost dashboards.
What you get
- →LLM API integration (OpenAI, Anthropic, Ollama)
- →Prompt design and iterative refinement
- →Structured output parsing with Pydantic
- →Context window management and conversation handling
- →Fallback and retry logic for API failures
- →Cost and latency optimisation
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