What role can prompt engineering play in preventing LLM hallucination, and what constitutes a good LLM prompt? Furthermore, how are OpenAI's models impacting this?
Prompt engineering and supervision are essential for successful implementation of Large Language Models (LLMs).
LLMs are known for providing varied responses, giving the feeling of being "alive". However, these variations can become excessive, resulting in false and inaccurate data, which is often referred to as hallucination.
To prevent this, prompt engineering and supervision can be improved through accurate prompt compilation, prompt structure, and observability.
Supervision & Observability
Stephen Broadhurst presented a very good talk recently at the European Chatbot Summit on supervision and observability from a LLM perspective.
You can read more about the basic principles here.
Accurate Prompt Compilation
A real-time conversational interface, such as a chatbot or voicebot, can be improved using a few-shot approach that provides the language model (LLM) with enough context and guidance to give accurate responses within the current dialog context.
The difficulty lies in quickly and accurately compiling the prompt from various data sources and submitting it to the LLM.
You can read more about the architecture for such an implementation here.
Prompt structure and compilation is of utmost importance. You can read more about basic prompt engineering principles here.
Text Generation Is A Meta Capability Of Large Language Models & Prompt Engineering Is Key To Unlocking It. You cannot talk directly to a Generative Model, it is not a chatbot. You cannot explicitly request a generative model to do something. But rather you need a vision of what you want to achieve and mimic the initiation of that vision. The process of mimicking is referred to as prompt design, prompt engineering or casting.
Prompts need to hold context and be contextually accurate. A good basic structure is shown below: