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Claude, GPT-4 and Gemini are the three most used AI models for business applications. Learn the practical differences and when to use each model.
Tokens determine what you pay for AI usage. Learn what tokens are, how to count them and how to control costs in business AI applications.
The Anthropic API provides access to Claude for business applications. Learn how to get started, what options are available and what the pitfalls are.
Vector databases are the core of RAG systems. Learn what they are, how they work and when you need one for your AI application.
Long AI workflows require smart context management. Learn how to store, compress and make context available without exceeding the context limit.
Open-source LLMs offer control and privacy. Closed-source models offer convenience and quality. Learn when to choose which and what the practical implications are.
Prompt caching reduces processing time and lowers API costs for repeated inputs. Learn how it works and when to use it.
Testing AI output is different from traditional software testing. Learn how to set up a systematic testing method for AI systems and what to measure.
AI hallucinations are a known problem with language models. Learn what they are, why they occur and how to limit them in business applications.
Grounding anchors AI output in factual sources. Learn what it is, how to apply it and why it is crucial for reliable AI applications.
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