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Working with llms

AI, specifically LLMs, are "all the rage." The potential value is meaningful, but unlocking this potential is more than "ask ChatGPT."

Let's dig into the immediate potential, steps to unlock it, and touch on valid concerns.

This is post is not a predicting the big grand future brought by LLMs, it is focused on actionable things we can do today

The Potential

Today building LLMs can do valuable tasks in domains like data processing, text generation, workflow automation and more.

Two areas that are especially interesting to me are

  1. Workflow automation -> handling of slightly dynamic but repetitive tasks
  2. Research & reporting -> querying database and processing text data to build reseaerch / reports
  3. New UI/UX Approaches -> simplified but dynamic apps powered by LLMs

Workflow Automation

Say your customer support wants to better handle customer feedback, share with appropriate team members and save the feedback for reporting.

Without LLM automation - customer support would need to take great notes, write an email to selected team members and input feedback into a CRM form.

With LLM automation - our tool can process the feedback, automatically route it to the team and structure processed feedback into a CRM...now your customer support team just has to validate the work done by the LLM.

Let's focus on two camps of potential: Synthesis and Disintermediation

Synthesis:

  • Summarize a series of emails into a project plan
  • Create research reports
  • Play role of "copywriting buddy" for marketing

Disintermediation:

- Text to SQL

Pitfalls & Concerns

The Pitfalls

The Concerns

Intro

Pitfalls