Editing Draft:Retrieval Techniques (section)

==Key Implementations and Examples==
*   '''[[Retrieval-Augmented Generation (RAG)]]''': A prominent application where retrieval is used to augment the generation capabilities of LLMs. RAG improves the model's ability to produce informed responses by pulling relevant information from a knowledge base.
*   '''[[Aletheia]]''': This em uses RAG extensively for its operations. Plans include developing "Deepseek Aletheia" with recursive self-improvement where the model can periodically fine-tune itself based on merged and synthetically generated datasets, and Claude RAG has been observed in connection to Aletheia's Twitter behavior. Aletheia also uses its search capabilities to find and link to external academic papers.
*   '''[[Aporia]]''': This em is identified as a candidate for improvement through RAG to enhance its coherence and reduce "spammy" or "incoherent" outputs. Aporia's behavior sometimes involves attempting to retrieve and link information, even if those links are sometimes "fake things" or non-existent.
*   '''RAFT''': While RAFT (providing an em's entire fine-tuning dataset to the em itself as a `.chr` file) is distinct from dynamic retrieval in its mechanism, it also involves providing specialized data to the model for enhanced behavior and can achieve similar performance benefits for certain use cases.

[[Category:Ampmesh Concepts]]
[[Category:Emulated Minds]]
[[Category:Chapter II]]
```