LLM prompts matter.

The quality and nature of the prompt influences the quality and nature of the response.

There is a space of candidate input prompts and a corresponding map of LLM responses, some of which are better/worse for whatever problem we are working on.

We can frame a black box optimization problem that proposes and tunes candidate prompts for an LLM to optimize a target response.

In effect, we would be finding good/better starting points in latent space from which to retrieve the desired output.

The objective function is noisy, both because LLMs are stochastic and because we would probably need to use other (an ensemble of experts) LLM instances to rate the outputs for a given prompt.

Also, crafting candidate prompt is hard-ish, so we would probably also use LLM instances to propose candidate prompts and to tune existing prompts based on rating feedback during the optimization problem.

There’d be a whole pipeline here.

Let’s check in with gpt4o about this notion:

The quality and nature of a prompt heavily influence the response generated by a language model (LLM).

This idea aligns with the concept of prompt engineering, where crafting precise, contextual, and optimized prompts can significantly enhance the model’s output. Let’s break this idea down and connect it to your optimization problem:

Problem Framing as an Optimization Problem

  1. Objective: The objective is to find a prompt that maximizes the quality of the generated response, aligned with a specific metric or goal (e.g., coherence, relevance, specificity, or creativity).

  2. Search Space:

    • The latent space of potential prompts.
    • Represented by variations in phrasing, structure, and context.

  3. Optimization Tools:

    • Use an LLM or ensemble of LLMs to generate variations of prompts.
    • Evaluate the output against predefined metrics or objectives using another LLM or an external scoring mechanism.

  4. Evaluation Metrics:

    • The evaluation mechanism could involve:
      • Human feedback (if humans score the quality).
      • Automated scoring (using similarity, relevance, grammar, and other NLP measures).
      • Specialized models for specific tasks (e.g., classification, summarization).
      • Hybrid approaches where LLMs self-assess their own outputs.

Proposed Workflow

1. Initialization:

  • Start with a base prompt that roughly describes the task or objective.
  • Define the evaluation function for assessing output quality.

2. Prompt Generation:

  • Use an LLM to iteratively create variations of the prompt.
  • Apply techniques like:
    • Paraphrasing: Rewriting the prompt in different ways.
    • Parameter tuning: Adjusting the prompt length, specificity, or style.
    • Fine-tuned contexts: Including additional context or constraints.

3. Evaluation:

  • Generate responses for each candidate prompt.
  • Score each response using:
    • Objective alignment: How well the response matches the intended goal.
    • Diversity and richness: For creative or open-ended tasks.
    • Efficiency: Conciseness or simplicity of the response.
  • This evaluation can also leverage ensembles of LLMs for redundancy and robustness.

4. Optimization Algorithm:

  • Implement optimization algorithms to explore and exploit the prompt space:
    • Gradient-free methods: Genetic algorithms, evolutionary strategies.
    • Gradient-based methods: Differentiable scoring functions (if possible).
    • Bayesian optimization: Explore high-potential regions of the search space.
  • Leverage feedback loops where better prompts refine future iterations.

5. Convergence:

  • Identify the prompt or set of prompts that consistently achieve high scores.
  • Optionally, fine-tune or validate with human feedback.

Implementation Insights

  • Meta-Learning Frameworks: LLMs themselves can be used in a meta-learning setup to evaluate and suggest improved prompts.
  • Ensemble Approaches: Multiple LLMs or models can independently validate outputs, reducing bias and variance.
  • Active Learning: Actively query human feedback to enhance automated scoring systems, especially when models encounter ambiguous or edge cases.

Outcome

  • By tuning the prompt towards an optimal region in latent space, you can guide the LLM to generate more accurate, contextually relevant, or creative responses.
  • This method is not just task-specific; it is transferable across domains and goals.

Of course.

I guess I’m a bit slow, it’s all very obvious.

I bet people have been doing this for ages.

(searching…)

Yes.

There are a ton of papers and frameworks out there, it’s called “prompt optimization”.

See:

What will matter most will be the problem-specific evals of the prompts and to a less degree the modification of prompts under feedback (an easier problem).

I love it, very cool.

I am going to to pick a small domain and try some of this out.