What We Learned Building O11y GPT: Part II

The blog post discusses the enhancements made to the O11y GPT model to improve its ability to assist engineers and observability users by refining its approach to answering queries. The improvements involve fine-tuning the model with specific focus on OPAL query language, combining keyword matching with embeddings to create an optimal corpus, crafting prompts with role definitions and response boundaries, and maintaining context-specific history in conversations. The team has also integrated this technology internally to gather data on performance and user feedback, guiding further product development. Additionally, the post touches on the phenomenon of "hallucinations" in language models, which sometimes reveal system feature gaps that can be addressed. The technology is described as being in its early stages, yet promising in enhancing user interaction, with the potential for improvements and new features like generating OPAL code in worksheets through GPT-4. The post concludes with an invitation for users to try out the new GPT-powered integrations.