Sunday, July 30, 2023
Many people utilize OpenAI's ChatGPT by inputting prompts one a time, (hopefully) assessing the generated responses, offering feedback, and repeating the process until the output aligns with their expectations. But have you ever wonder how organizations tackle highly complex questions from different perspectives quickly? This guide offers a streamlined explanation on how we can efficiently link Language Learning Models (LLMs) together to expedite this process and substantially enhance it with trained data. It's important to recognize that user-provided data is pivotal in enabling these systems to understand and respond accurately. The interaction begins when users submit their information, which could range from a simple query to a complex directive.
In this "flow", once we have user input, it is passed on to a LLM who's job is to decide which fine-tuned model will be most effective in handling this particular input. Think of it as a guide traffic officer at an intersection, directing cars (or in this case, user inputs) to their appropriate destinations.
For our demonstration, we'll consider three types of fine-tuned models:
The nature of user input determines which one of these models will be best suited for responding.
Once routed by the LLM, each selected model generates its response based on its training and understanding of the given input. Think about it like asking an expert for advice – they draw upon their specific knowledge and experience to give you an informed answer.
Finally, after all these steps have been completed smoothly and efficiently in fractions of seconds - voila! The generated response is delivered back to you, providing personalized feedback or suggestions based on your initial input.
But what if we could add another layer of optimization to this process? What if we could ensure that each individual response not only maintains coherence but also relevance and quality in accordance with the our initial query? This is where the concept of an Evaluation LLM comes into play.
I've introduced an additional level of check and balance to the model - the Evaluation LLM. After each selected model generates its response, these are not immediately combined into a report or output and given to the user. Instead, they are passed onto to this wonderful gatekeeper.
The role of the Evaluation LLM is crucial. It assesses each response for coherence, relevance, and quality before giving its stamp of approval. Think of it as a quality controller at a manufacturing unit, ensuring that every product passing through meets the set standards.
The advantage of incorporating an evaluation stage via an evaluation language model (LLM) is twofold:
Once approved by the Evaluation LLM, these responses are then combined into a comprehensive report providing insights from multiple perspectives as per your original query - thus adding depth and breadth to your response!
So there you have it! An enhanced flow chart which not only allows for complex queries requiring multiple areas of expertise but also ensures high-quality responses by introducing an extra layer of evaluation via an Evaluation Language Model (LLM).
By embracing this approach, users can be assured that their highly complex prompts will generate richly detailed and thoroughly evaluated outputs - making their interactions with language models more effective and insightful than ever before!
If you found this post useful and want to learn more, contact me about prompt engineering.
And if you're interested in custom Prompt Training for you or your organization, I'm available!