AI That Ships to Production

Claude Sonnet/Opus for complex reasoning, long context, and coding. GPT-4 Turbo for broad ecosystem and vision. Open-source (Llama 3, Mistral) for cost at scale or data privacy. Most production systems route between 2-3 models — small tasks to cheaper models, complex to premium. We benchmark on YOUR use case, not leaderboards.

Four levers: (1) prompt caching for repeated context (Anthropic's caching cuts costs 75% for heavy system prompts), (2) model routing (Haiku/GPT-4o-mini for routine, Sonnet/GPT-4 for hard), (3) output token limits + early-termination prompts, (4) usage dashboards per user/feature. Typical production deployments spend ₹15-80K/month on LLM costs depending on volume.

No silver bullet, but layered defenses: (1) RAG with strict citation requirements — model must cite source passages, (2) structured output with JSON schema validation, (3) evals before shipping and monitored in production, (4) guardrails (Llama Guard, Constitutional AI principles), (5) human-in-the-loop for high-stakes outputs. We treat hallucination reduction as an ongoing operation, not a one-time setup.

Yes, but usually we recommend prompt engineering + RAG first — they cover 90% of use cases without fine-tuning complexity. When fine-tuning genuinely helps (consistent output format, domain-specific tone, small-model performance lift), we fine-tune on OpenAI, fine-tune open-source models on Together/Replicate, or fine-tune via LoRA for self-hosted.