Cost, Latency & Quality Tradeoffs

LLM & Agentic

The cost analysis framework for fine-tuning decisions — from GPT-4o to self-hosted distilled models, with ROI calculations.

Cost, Latency & Quality Tradeoffs

Fine-tuning decisions are fundamentally business decisions. The engineering question is "can we fine-tune?" The business question is "should we fine-tune?"

Cost-Quality-Latency Tradeoff Space

GPT-4o / Claude Sonnet Tier 1

~$15/1M input tokens

High quality, high cost

Quality: ★★★

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LLM Lifecycle

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Complete lifecycle of large language models from pre-training through fine-tuning, RLHF, and deployment — with architecture diagrams and production considerations.

How LLMs Call Tools

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How LLMs use function calling and tool use — the mechanics behind tool-calling agents, from prompt engineering to structured output.

When to Fine-Tune: The Decision Framework

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Should you fine-tune at all? A structured decision framework for prompt engineering vs RAG vs fine-tuning.

LoRA, QLoRA & PEFT Methods

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How LoRA works inside transformer layers, QLoRA for memory-efficient training, and the full PEFT method comparison with code examples.

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