My Take on LLMs

My career began in Natural Language Processing, and I’ve studied the mathematics and theories behind AI extensively. So when ChatGPT took the world by storm, I approached it with skepticism. I viewed Large Language Models (LLMs) as sophisticated “next word predictors” – black boxes that ingest vast amounts of data and generate plausible word sequences from initial prompts, and felt that The hype surrounding LLMs seemed overblown. I struggled to see how these “predictors” could revolutionize our work and lives. However, curiosity and a touch of boredom eventually led me to explore the world of LLMs.

Dumb Code Generator, Reading Partner, and Code Kick-starter

My first practical application of LLMs came when I faced the task of creating numerous Golang API endpoints with similar structures but minor differences. These attributes made deduplication challenging. To my surprise, I found LLMs incredibly useful for generating that kind of predictable with little twists code. LLMs’ code generation ability saved time and reduced errors in these tedious tasks.

Experimenting with LLM-aided reading was a revelation. The experience surpassed traditional reading in many ways:

• It offered a more interactive learning process.
• LLMs could reference additional sources for deeper explanations.
• It enabled real-time interrogation of the text.

The approach of using LLMs as reading partner made reading complex texts more accessible and engaging. For instance, when I needed to skim through lengthy, complex regulation documents, using LLMs to summarize the text and answer follow-up questions proved highly effective.

Taking it a step further, I began using LLMs as first draft writers and project kick-starters. They excelled at initiating writing projects and providing a solid foundation for side projects. I could request initialization commands and receive a “good enough” project structure to build upon.

Limitations

Despite their benefits, LLMs have significant limitations. To understand these, let’s simplify the LLM working model into two components:

• “Short-term memory”, more commonly referred as context length, which store what the LLMs can refer to directly right now, and
• “Long-term memory”, which is the synthesis of all data used to create the LLM (books, internet comments, articles, etc.) ¹.

The limited context length means LLMs struggle with longer documents or keeping large codebases in mind. While we can work around this by dividing knowledge into smaller pieces or creating summarizations, the limitations of the “long-term memory” are more challenging to overcome.

There’s a wealth of tacit and “esoteric” knowledge that LLMs don’t possess in their “long-term memory.” Creating new, nuanced, and complex code, especially in niche domains where knowledge isn’t widely available, remains a significant challenge for LLMs. Even when writing this “relatively simple” blog post, I found that while LLMs can produce a decent first draft, they struggle to fully capture personal experiences or develop more complex arguments without making the process feel convoluted ².

Useful LLM Tools and Notes

• Supermaven: I’ve found its focus on rapid code generation particularly useful for producing straightforward code. The free version has been sufficient for my needs, and I highly recommend it.
• Claude: I use Claude for reading assistance and even drafted parts of this post with it. Compared to ChatGPT, I appreciate its longer context (200,000 tokens comparing to 128,000 tokens), and its more precise recall abilities (Claude was able to pinpoint an almost exact location in a regulation document I provided, while ChatGPT couldn’t). For documents exceeding Claude’s context length, splitting them into manageable parts (e.g., dividing a large book into chapters) and requesting summarizations works well.
• ChatGPT: The tool excels at simpler tasks like improving wording or general brainstorming.

Final Thoughts

Despite their current limitations, LLMs have become an invaluable tool in my workflow. They’ve transformed how I approach various tasks, from coding to writing and researching. I’m excited about future developments in this field. While I believe LLMs can aid ³ us in various tasks, I don’t foresee them replacing human judgment anytime soon.

Footnotes