Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
📰 ArXiv cs.AI
A survey of contextual enrichment strategies for large language models, including in-context learning, prompt engineering, and retrieval-augmented generation
Action Steps
- Understand the limitations of large language models, including static knowledge and finite context windows
- Explore in-context learning and prompt engineering techniques to improve model performance
- Investigate Retrieval-Augmented Generation (RAG) and GraphRAG for more structured context supply
- Apply causal retrieval-augmented generation for more effective causal reasoning
Who Needs to Know This
Researchers and engineers working on large language models can benefit from this survey to improve their models' performance and capabilities, while product managers and AI engineers can apply these strategies to develop more effective AI-powered products
Key Insight
💡 Contextual enrichment strategies can significantly improve the performance and capabilities of large language models
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🤖 Improve LLMs with contextual enrichment strategies! 📚
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