Tag: RAG
Debugging Prompts: Systematic Methods to Improve LLM Outputs
Learn systematic methods to debug LLM prompts, from task decomposition and RAG to mathematical steering, to ensure reliable and accurate AI outputs.
Prompt Engineering for Large Language Models: Core Principles and Practical Patterns
Prompt engineering is the art of crafting precise inputs to get the best results from large language models. Learn core principles like few-shot prompting, chain-of-thought, and RAG-and how small changes in wording can dramatically improve AI output.
Hybrid Search for RAG: Boost LLM Accuracy with Semantic and Keyword Retrieval
Hybrid search combines semantic and keyword retrieval to fix RAG's biggest flaw: missing exact terms. Learn how it boosts accuracy for code, medical terms, and legal docs-and when to use it.
