Explore large language model architectures, fine-tuning strategies, prompt engineering, and how LLMs power modern AI applications.
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A detailed cost comparison of self-hosting open-source LLMs versus using closed API providers, covering infrastructure, engineering, quality, and hidden costs.
Practical techniques to reduce LLM inference costs by 40-80 percent through prompt caching, semantic caching, and KV cache optimization in production systems.
A technical primer on how reasoning models work — from basic chain-of-thought prompting to OpenAI's o3 and DeepSeek R1. Understanding the inference-time compute revolution.
A practical 6-step framework for selecting the best large language model for your application based on performance, cost, latency, and business requirements.
Learn the three critical LLM evaluation methods — controlled, human-centered, and field evaluation — that separate production-ready AI systems from demos.
Explore how small language models (1-7B parameters) are closing the gap with frontier models for production use cases — from Phi-4 to Gemma 2 and Mistral Small.
How combining knowledge graphs with LLMs enables structured reasoning that overcomes hallucination, improves factual accuracy, and unlocks complex multi-hop question answering.
The RAG vs fine-tuning debate continues to evolve. A clear framework for deciding when to use retrieval-augmented generation, when to fine-tune, and when to combine both.
