LLMs Demand Observability-Driven Development | Honeycomb
LLMs demand we modify our behavior and tooling in ways that will benefit even ordinary, deterministic software development. Find out why.| Honeycomb
Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models. For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token. What mig...| Thinking Machines Lab
LLMs demand we modify our behavior and tooling in ways that will benefit even ordinary, deterministic software development. Find out why.| Honeycomb
In this blog I share my experience in building a Python REPL augmented with ChatGPT. I explore how the application is built, and speculate on software engineering patterns and paradigms that might emerge in systems built on Large Language Models (LLMs). GEPL - Generate, Evaluate, Print, Loop Link to this section Introduction The Lisp programming language made REPLs (Read, Evaluate, Print, Loop) famous. REPLs are interactive programming environments where the programmer gets immediate feedback...| isthisit.nz
