Prompt Hub Strategy

Here is a step-by-step strategy to build a “Prompt Hub”:

⸻

1. Input Preparation

a. Source Validation Guidelines
	•	Collect existing model validation templates and guideline documents.
	•	Parse each template into sections (e.g., Model Framework, Performance, Assumptions).
	•	Extract each guideline instruction within these sections (e.g., “Assess whether the model objectives align with core model requirements”).

⸻

2. Prompt Generation

a. Language Model Setup
	•	Use DeepSeek-RL-distilled-Qwen-7B via a local inference server (e.g., Hugging Face Transformers or vLLM).

b. Generate Multiple Prompts per Guideline

For each guideline:
	•	Construct a generation prompt like:

Instruction: Assess whether the model objectives align with the core model requirements.
Generate a prompt that can be used to instruct an LLM to perform this task.


	•	Generate 3 prompt variants per guideline using temperature sampling (e.g., temperature=0.7).

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3. Prompt Ranking Using Log-Likelihood

a. Compute Log-Probability for Each Prompt
	•	Use the same LLM to compute log-likelihood for each generated prompt.

# Pseudocode
model.compute_log_likelihood(prompt)


	•	This score estimates how natural/confident the model is in producing that text.

b. Sort Prompts by Log-Likelihood
	•	Rank the 3 variants for each guideline based on descending log-likelihood.
	•	Keep the top N if needed.

⸻

4. Structure into a Prompt Dictionary

a. Define Data Structure

prompt_hub = {
    "Template Name": {
        "Section Name": [
            {
                "guideline": "...",
                "prompts": [
                    {"text": "...", "log_likelihood": -12.3},
                    {"text": "...", "log_likelihood": -13.5},
                    ...
                ]
            },
            ...
        ]
    }
}


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5. Output Format and Handoff

a. Save the Prompt Hub
	•	Serialize the dictionary as a JSON or Python pkl file.
	•	Optionally convert to CSV for easy UI prototyping.

b. Handoff to Front-End Team
	•	Provide API-ready structure or file for downstream integration into:
	•	Prompt selection UI
	•	Prompt recommendation systems
	•	Auto-fill interfaces for model validation workflows

⸻

# prompt_hub_builder.py

import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
from collections import defaultdict
from typing import List, Dict

# === 1. Load Model and Tokenizer ===

def load_model(model_name="deepseek-ai/deepseek-llm-7b-chat"):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
    model.eval()
    return tokenizer, model

# === 2. Generate Prompt Variants ===

def generate_prompts_for_guideline(guideline: str, tokenizer, model, num_variants=3, temperature=0.7) -> List[str]:
    prompt_template = f"Instruction: {guideline}\nGenerate a prompt to instruct an LLM to perform this task."
    input_ids = tokenizer(prompt_template, return_tensors="pt").input_ids.to(model.device)
    
    outputs = model.generate(
        input_ids=input_ids,
        max_new_tokens=50,
        num_return_sequences=num_variants,
        do_sample=True,
        temperature=temperature
    )
    return [tokenizer.decode(output, skip_special_tokens=True).split("Instruction:")[-1].strip() for output in outputs]

# === 3. Score Prompts Using Log-Likelihood ===

def compute_log_likelihood(prompt: str, tokenizer, model) -> float:
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model(**inputs, labels=inputs["input_ids"])
        loss = outputs.loss.item()
    return -loss * inputs["input_ids"].shape[1]  # Log-likelihood approximation

# === 4. Build Prompt Hub ===

def build_prompt_hub(validation_data: Dict[str, Dict[str, List[str]]], tokenizer, model) -> Dict:
    prompt_hub = defaultdict(lambda: defaultdict(list))
    
    for template, sections in validation_data.items():
        for section, guidelines in sections.items():
            for guideline in guidelines:
                prompt_variants = generate_prompts_for_guideline(guideline, tokenizer, model)
                scored_prompts = [
                    {
                        "text": p,
                        "log_likelihood": compute_log_likelihood(p, tokenizer, model)
                    }
                    for p in prompt_variants
                ]
                scored_prompts.sort(key=lambda x: x["log_likelihood"], reverse=True)

                prompt_hub[template][section].append({
                    "guideline": guideline,
                    "prompts": scored_prompts
                })
    return prompt_hub

# === 5. Example Usage ===

if __name__ == "__main__":
    # Example validation data format
    validation_templates = {
        "Credit Risk Template": {
            "Model Framework": [
                "Assess whether the model objectives align with the core model requirements.",
                "Evaluate the appropriateness of the model development methodology."
            ],
            "Performance": [
                "Determine whether the model meets performance thresholds on test data."
            ]
        }
    }

    tokenizer, model = load_model()
    prompt_hub = build_prompt_hub(validation_templates, tokenizer, model)

    # Print or save to JSON
    print(json.dumps(prompt_hub, indent=2))