What to do when fine-tuning is not working?

[lmccallum]

These resources should help:

Introducing Embeddings

Hi all! We’re rolling out Embeddings to all API users as part of a public beta. Our Embeddings offering combines a new endpoint and set of models to address more advanced search, clustering, and classification tasks. The /embeddings endpoint returns a vector representation of the given input that can be easily consumed by machine learning models and algorithms. We are releasing three sets of models: Text Similarity: excels in capturing semantic similarity between pairs of text Text Search: …

OpenAI Cookbook

Text Similarity Models - Embedding API Query

Hi - I am using the Text Similarity Models - Embedding API to compute similarity score between two words. I am referring to the code snippet from this blog (copied below) - Introducing Text and Code Embeddings in the OpenAI API The result is a “similarity score”, sometimes called cosine similarity between –1 and 1, where a higher number means more similarity. The similarity score is in decimals and pretty close to each other. e.g. Castle – Palace - the score is 0.88 Building – Palace - the s…

Contextualizing completions: fine-tuning vs. dynamic prompt engineering using embeddings

Hi everyone and apologies for the long post. Just trying to give as much info as possible. A little background on what I’m trying to do: I would like to generate completions based on the context of a specific project the company is working on. For example, say the company is working on multiple software development projects. Each project has its own set of artifacts (e.g., requirements, project schedule, documentation, user-generated content, etc.). If a developer on Project XYZ asks, for examp…