and prompt generation. If you’re an R user and interested in RAG, keep an eye on ragnar. Serious LLM users will likely want to code certain tasks more than once. Examples include generating ...

SEARCH-R1 trains LLMs to gradually think and conduct online search as they generate answers for reasoning problems.

DSPy shifts the paradigm for interacting with models from prompt hacking to high-level programming, making LLM applications far easier to maintain and optimize.

Unlike traditional machine learning models ... Use Retrieval-Augmented Generation (RAG): Instead of fine-tuning an LLM on financial documents, RAG allows models to retrieve relevant financial ...

With pure LLM-based chatbots this is beyond question, as the responses provided range between plausible to completely delusional. Grounding LLMs with RAG reduces the amount of made-up nonsense ...

Requirement-Oriented Prompt Engineering (ROPE) helps users craft precise prompts for complex tasks, improving the quality of LLM outputs and driving more efficient human-AI collaborations.

Prompt Security launches comprehensive Authorization features for enterprise GenAI applications, enabling granular, context-aware access control as queries are made Addresses critical security gap ...