Best practices for LLM bots
Prompt refinement (or “prompt engineering”)
Prompt engineering involves crafting and refining the input instructions in a way that guides the LLM bot model to generate desired and specific responses. It entails formulating prompts that elicit the desired information or behavior from the LLM bot. This process often involves understanding the bot’s capabilities, tweaking the wording of prompts, and experimenting with different approaches to achieve the intended outcomes. The goal is to fine-tune the prompts to improve the model's performance and generate responses that align more closely with the user's expectations. Prompt engineering is an iterative process, and adjustments are made based on the observed outputs and user interactions with the system
Pro tips for refining and engineering your prompt:
Be Specific and Explicit: If you want the bot to always do something, you need to clearly define the information or task you expect the bot to perform. Try rephrasing the language to “must” and “always”. Ambiguous prompts can lead to varied responses – using passive or less direct language like “should” or “try to” will leave too much ambiguity such that the bot will decide when they do something.
Use Contextual Information: Always leverage relevant context to enhance the bot’s understanding. You should provide context in the prompt that helps the bot better comprehend the specific scenario or topic.
Provide Examples: Offering specific examples can guide the model's understanding. Include instances or examples related to your query to help the model grasp the context more effectively.
Experiment with Formatting: The structure of the prompt can influence the output. Try different formats, such as changing the order of words or using bullet points, to see how it affects the model's response.
Fine-Tune Prompt Length: Longer prompts may be more informative but risk confusing the bot. Optimize prompt length based on the complexity of the task, finding a balance between providing sufficient information and avoiding unnecessary complexity.
Ask ChatGPT to optimize your prompt: You can always run your prompt through ChatGPT by asking it to optimize it for an LLM Chatbot.
Iterative Refinement and testing: You should continuously refine prompts based on model outputs. Analyze LLM Chatbot logs, identify shortcomings in its thought process and execution, and adjust prompts accordingly to improve over time.
Experiment with different models: The choice of OpenAI models significantly influences an LLM Chatbot's behavior. Models like GPT-3.5 have vast knowledge, while newer models might have updated information. GPT-4 may offer improved performance, while a smaller model could be more cost-effective. Keep an eye out for updates in models and adjust according to your needs.
Knowledge base refinement
LLM Chatbots are only as good as the knowledge they are equipped with. To make your bot more effective, it's crucial to manage and refine your knowledge base content. This knowledge base is the brain of the bot, providing the information it needs to understand language and generate responses for your customers. It ensures the bot gives accurate and consistent information to your customers.
Pro tips for refining your knowledge base:
Structured and Comprehensive Knowledge Base: Ensure that your knowledge base is well-structured and covers a wide range of topics related to your product or service. This provides a solid foundation for the language model to draw information from when responding to user queries.
Regular Updates and Maintenance: Keep your knowledge base content up-to-date. Regularly review and update articles to reflect changes in your product or service. This ensures that the AI has access to the most current information, improving the accuracy of its responses. You should assign a knowledge base manager to maintain it as an accurate and update to date source of truth.
Identify gaps from bot logs: Establish where there may gaps in your knowledge base by reviewing your LLM bot logs. Here you can see the thought process of your bots, which articles they searched, and the answers they gave.
Last updated