Dataknobs

Define persona, context, different output length. By crafting specifc prompts, you can guide LLM powering the chatbot to generate responses that are relevant, aligned, natural and meet the requirements.

You can use same prompt setting but compare different LLMs like OpenAI, Gemini, Claude, Llama or your cusom LLM. Compare and see the result to determine which LLM meet your requirement and is cost effective.

To leverage personalziaton effectively, you need to experiment with different user attributes like demographics, past interactions, document uploaded, information shared etc. Include contextual information to determine what works best.

You can compare output from different vector DBs and determine which works. AB Experiment and KREATE provide consistent interface to use different vector DB. For same prompt determine what context is given by vector DB.

Different embeddings encode text data in different ways. Some embeddings use context of word within sentence. Experimenting with embeddings allows you to determine approach that captures the nuances of user language e.g. slang, sarcasm, sentiment.

Chunk size is length of text segments LLM processes at a time. Large chunksize provide more context and help in getting informative responses. At same time it is expensive and slow. Smaler chiunk size are most efficent as they focus on relevant part of query.

Simulate various scenarios by providing diverse inputs and configurations. Meticulously document the resulting responses and any errors encountered. Subsequently, develop effective workarounds and permanent fixes to address these identified issues.

Develop mechanism for chatbot to develop its reasoning and decision making process. Once satisifed you can include in chatbot to build trust and help users understan chatbot limitations.