Large language models have gone from research curiosity to production necessity in record time. But there's a gap between a compelling demo and a reliable product feature. Here's what we've learned from integrating LLMs into production applications.
Start with the Problem, Not the Technology
The most common mistake we see: teams decide they want to "add AI" and then look for problems to solve. This leads to features that are impressive in demos but don't move business metrics.
Instead, start with a user problem that has these characteristics:
High volume. The task happens frequently enough that automation has real impact.Tolerance for imperfection. LLMs are probabilistic. Tasks where a 95% accuracy rate is valuable (content suggestions, summarization, classification) work much better than tasks where 99.9% accuracy is required (financial calculations, medical diagnoses).Easy to verify. Users can quickly tell if the output is good. This enables human-in-the-loop workflows and continuous improvement.Architecture Decisions
Prompt Engineering vs. Fine-Tuning
For most applications, well-engineered prompts with retrieval-augmented generation (RAG) are the right starting point. Fine-tuning makes sense when:
You need consistent output format that prompting can't reliably achieveYour domain language is specialized enough that base models struggleYou've validated the use case and need to reduce per-request costs at scaleRAG Done Right
Retrieval-augmented generation is the most common pattern for adding domain knowledge to LLMs. Key decisions:
Chunking strategy matters. Chunk by semantic boundaries (paragraphs, sections), not by fixed token counts. Overlap chunks slightly to preserve context.Embed with care. Use embedding models appropriate for your content type. Test retrieval quality before building the full pipeline.Hybrid search wins. Combine vector similarity with keyword search. Pure vector search misses exact matches; pure keyword search misses semantic similarity.Cost Management
LLM API costs can spiral quickly. Practical strategies:
Cache aggressively. Many requests have identical or near-identical prompts. A semantic cache can cut costs 30-50%.Use the smallest model that works. GPT-4 class models for complex reasoning, smaller models for classification, extraction, and simple generation.Stream responses. Better user experience and you can stop generation early if the output is going off-track.Production Concerns
Latency
LLM calls are slow compared to traditional APIs. Design for it:
Show streaming output when possibleUse optimistic UI patternsPre-compute results for predictable queriesSet aggressive timeouts and have fallback behaviorReliability
LLM APIs go down. They return errors. They occasionally produce nonsensical output. Your application needs to handle all of these gracefully:
Implement retries with exponential backoffHave fallback providers (e.g., Claude as backup for GPT, or vice versa)Validate output structure before using itLog everything for debugging and improvementEvaluation
You can't improve what you can't measure. Build an evaluation framework early:
Define metrics that map to user value (not just model accuracy)Build a test set of real-world examples with expected outputsRun evaluations automatically on prompt or model changesTrack production quality metrics over timeSecurity and Privacy
LLMs introduce new attack surfaces:
Prompt injection. User input that manipulates the model's behavior. Sanitize inputs, use system prompts, and validate outputs.Data leakage. Don't put sensitive data into prompts sent to third-party APIs unless you have appropriate agreements in place.Output filtering. Models can generate harmful content. Implement output validation appropriate for your use case.The Bottom Line
LLMs are genuinely useful — when applied to the right problems with appropriate engineering rigor. Treat them as a powerful but imperfect tool, build guardrails, and iterate based on real user feedback.
AILLMmachine learningRAGproduction AI