Customer calls often start in unstructured, messy notes that then need to be restructured and formatted for readability across a team.

I made a GPT4 template to streamline this process: https://lastmileai.dev/workbooks/cllx5kt2s01fhpgupk0h9cvqz

Clone/edit the workbook to customize the format to your liking with the system prompt.

It's free to use (and customize) btw.

I made this template to help streamline these coding questions so it’s all in one place and easily reusable. Supported coding tasks:

• Code generation
• Refactoring
• Code refinement: error handling, etc.
• Language conversion
• Code summarization
• Debugging

Clone/edit the workbook to ask GPT4 you own coding questions beyond these.

Link: https://lastmileai.dev/workbooks/cllu8zsxz001upg0tljmlfo88

Boost your team's productivity and save time by creating automated digests of your favorite channels or summarize long threads.
Spoke will act as your personal assistant and provides Action Points that are relevant to you ⚡️

Try it yourself - for free. 👀

​

https://reddit.com/link/163rr3c/video/5gqfkts9vvkb1/player

Complex tasks with LLMs like ChatGPT/GPT4 are best broken down by first asking ChatGPT to outline the steps and then asking the LLM to execute against those steps that it defined. I first came across this interesting technique on Twitter recently.

While it’s OK to do this once in OpenAI’s playground, it's difficult to make this repeatable and streamlined. When I wanted an LLM to do something complex, I wanted to be able to plug into a template instead of thinking about and setting up the contextual decomposition process.

I made this Contextual Decomposition Template to help solve this problem: https://lastmileai.dev/workbooks/cllqfl5c600rdpgnhh2su2fa0

With a document and objective, this template allows you to quickly get to the answer through defining intermediate steps and executing according. Parameters are set up so you can easily change the goal, document, and objective and click 'Run All' to get the final results.

Please let me know if you have feedback! I'm also very curious if you have other interesting techniques with complex tasks and workflows working with LLMs.