Understanding the Context and Logic of Language Model Chaining

The language model (LLM) takes a context along with the prompt and additional information, which is then fed into the second model. The context includes system prompt, user prompt, and a set of the rag retrieval, which is dependent on the initial embedding model. The quality of each step in the chaining process is essential, as it involves multiple language models to iteratively build a smart context. The vector lookup is crucial for building context, but other forms of lookup are also important. The intersection of the vector database and the system prompts, along with the logic around it, plays a significant role in language model chaining.