4 ms·
That’s pretty typical, though not especially reliable. (Allthough in my experience, Gemini currently performs slightly better than ChatGPT for my case.) In one
by fwn 1y ago
That’s pretty typical, though not especially reliable. (Allthough in my experience, Gemini currently performs slightly better than ChatGPT for my case.)
In one repetitive workflow, for example, I process long email threads, large Markdown tables (which is a format from hell), stakeholder maps, and broader project context, such as roles, mailing lists, and related metadata. I feed all of that into the LLM, which determines the necessary response type (out of a given set), selects appropriate email templates, drafts replies, generates documentation, and outputs a JSON table.
It gets it right on the first try about 75% of the time, easily saving me an hour a day - often more.
Unfortunately, 10% of the time, the responses appear excellent but are fundamentally flawed in some way. Just so it doesn't get boring.
- simonw 1y agoTry reformatting the data from the markdown table into a JSON or YAML list of objects. You may find that repeating the keys for every value gives you more reliable results.
- fwn 1y agoThanks for the suggestion! I’ll start benchmarking my current md table setup against one using YAML. It's apparently slightly less verbose than JSON.
- v3ss0n 1y agoGemini does a lot better at long context.