A Simplified Guide to Interactions Between Language Models Using Buyer Personas - Prompt Engineering

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.

Utilizing the Language Learning Model (LLM)

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.

Understanding the Different Fine-Tuned Models using Buyer Persona's

For our demonstration, we'll consider three types of fine-tuned models: 

  1. Marketing Analysis LLM: This model takes on the role of a marketer. It evaluates the buyer persona from a marketing perspective, analyzing their preferences and behaviors to help create more targeted marketing strategies.
  2. Sales Strategy LLM: Here's your virtual salesperson. This model analyzes the buyer persona from a sales perspective. It considers factors like buying patterns and decision-making processes to suggest effective sales strategies.
  3. Product Development LLM: Acting as a product developer, this model assesses the buyer persona from a product development standpoint. By understanding what features or aspects are important to users, it aids in designing products that better meet their needs.

The nature of user input determines which one of these models will be best suited for responding.

Here's a brief outline of the steps (graphic incoming! Sorry, Bit Behind!)

  1. 'User Input' is collected.
  2. This 'User Input' is passed to an LLM that decides which fine-tuned model is most appropriate to handle the input.
  3. Again, these models and tasks are:
  4. Depending on the nature of the user input, it will be routed to one of these models.
  5. The chosen model generates a response based on its training and understanding of the input.
  6. This response is then given back to the user.

From Input Routing to Response Generation

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.

Delivering User-Centric Responses

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.

Could We Optimize This Further? YES.

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.

Introducing Evaluation LLM

  1. 'User Input' is collected, such as "evaluate this buyer persona from three different perspectives, providing pros and cons".
  2. This 'User Input' is passed to an LLM that decides which fine-tuned model(s) is most suitable to handle the input. In this case, it might involve multiple models.
  3. Which again are:
  4. The user input will be routed to each of these three models either in succession or simultaneously depending on the implementation.
  5. Each selected model generates a response based on its training and understanding of the input:
  6. Inserted Evaluation Step: Each model's response is then evaluated by another LLM (which we can call an 'Evaluation LLM') before being combined into a report. This Evaluation LLM ensures that each individual response maintains coherence, relevance and quality in accordance with the initial user query.
  7. Evaluation Continued: The approved responses are then combined into a comprehensive report that provides an evaluation of the buyer persona from three different perspectives.
  8. This final coherent and quality-controlled response report is then presented back to the user.

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 Evaluation LLM

The advantage of incorporating an evaluation stage via an evaluation language model (LLM) is twofold:

  1. Quality Assurance: By introducing this extra layer of evaluation, we can ensure high-quality responses. Each output from the various fine-tuned models is subjected to rigorous scrutiny by the Evaluation LLM to maintain a consistent standard throughout.
  2. Coherence Maintenance: It ensures that all responses align with one another and maintain coherence with respect to the original user input query. This prevents, or, at least, minimizes any misalignment or inconsistencies across responses from different models.

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.

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