Which OpenAI Model Should I Use for a Job-to-Resume Matching POC?

Below is a summary of key points and recommendations based on current API capabilities:

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Model Choice for the POC

• Fine-Tuned GPT‑3.5‑Turbo:
  For a proof of concept where you plan to process structured data (i.e., matching resumes to a job description), a fine‑tuned version of GPT‑3.5‑Turbo is a strong candidate. Fine‑tuning on a well‑defined task (such as resume matching) can yield efficient performance while keeping costs lower than using GPT‑4 for every request.

• When to Use GPT‑4:
  GPT‑4 typically shines in open‑ended applications that require broader contextual understanding and nuanced reasoning. For your POC where the task is fairly structured, GPT‑3.5‑Turbo (fine‑tuned if needed) should suffice.

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GPT‑4 Enterprise and Fine-Tuning

• Fine-Tuning Availability:
  At present, fine‑tuning is supported for select models (including specific versions of GPT‑3.5‑Turbo and a few GPT‑4 variants), but the GPT‑4 Enterprise solution generally offers access to the advanced model for chat and completions without exposing fine‑uning capabilities. In other words, you cannot fine‑tune GPT‑4 Enterprise out‑of‑the‑box to add tens of thousands of resumes directly.

• Adding Large Data Sets:
  If your goal is to incorporate tens of thousands of resumes in a fine‑tuned setup, the current fine‑tuning support is focused on those models that explicitly allow customization (like GPT‑3.5‑Turbo‑based models and certain GPT‑4 variants, not the general Enterprise product).

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Additional Recommendations

• Consider Embeddings for Resume Matching:
  For large-scale resume data, a common architecture is to pre‑compute embeddings (using models such as “text‑embedding‑ada‑002”) for each resume and store them in a vector database. Then, when a job description is provided, you can compute its embedding and use similarity search (e.g., cosine similarity) to retrieve the most relevant resumes. This approach is often more efficient and scalable than fine‑uning a chat model to ingest all resumes.

  PYTHONCopied
  # Example pseudocode for generating embeddings using OpenAI's API
  import openai
  
  # Assuming you have a list of resumes in `resumes`
  embeddings = []
  for resume in resumes:
      response = openai.Embedding.create(
          model="text-embedding-ada-002",
          input=resume
      )
      embeddings.append(response["data"][0]["embedding"])
  
  # Store embeddings in your vector database for similarity search.

• Hybrid Approach:
  You might also consider a two‑step process:

  1. Similarity Search: Use embeddings to quickly narrow down a candidate set of resumes.
  2. Fine-Tuned Model for Nuanced Matching: Use a fine‑tuned GPT‑3.5‑Turbo to rank or refine the candidate list based on additional context from the job description and resume details.

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Summary Answer

• For an initial POC: A fine‑tuned version of GPT‑3.5‑Turbo is a good option given the structured nature of resume matching and lower operational cost.
• GPT‑4 Enterprise: This solution currently does not offer fine‑tuning capabilities to add thousands of resumes. It is optimized for general-purpose, advanced reasoning tasks.
• Other Ideas:
  • Leverage pre‑computed embeddings (e.g., using “text‑embedding‑ada‑002”) with a vector database for scalable resume matching.
  • Consider a hybrid search and refinement approach combining vector search with a fine‑tuned model for best results.

Feel free to ask additional technical questions or clarifications on using any of these APIs!