688: Six Reasons Why Building LLM Products Is Tricky — with Jon Krohn (@JonKrohnLearns)

Processing Speed

Jon Krohn highlights the slow processing speed of large language models (LLMs) as a significant challenge in product development. Despite using multiple GPUs, the largest models can take tens of seconds to generate long outputs, which may be acceptable for real-time reading but not for other applications like summarizing job candidates 1. This delay can be problematic when LLM outputs serve as intermediates for further processing, potentially causing users to wait minutes for results. Jon suggests solutions like reducing model size through techniques like distillation, pruning, and quantizing 1.

LLMs being slow is a problem.

--- Jon Krohn

These methods can help improve speed without significantly compromising performance 2.

   

Context Windows

Context window limitations in LLMs present another hurdle, as they define the amount of text a model can handle at once. Jon Krohn explains that increasing the context window requires exponentially more computational power, which can slow down processing significantly 2. This is particularly challenging for applications needing extensive input or output, such as summarizing long resumes. While models like GPT-4 and Claude offer larger context windows, they also introduce issues like slower performance and hallucinations 2.

Increasing your context window just a bit, you're drastically increasing the amount of compute required.

--- Jon Krohn

Finding a balance between context window size and processing efficiency remains a complex task 1.

   

Prompt Engineering

Prompt engineering is crucial yet complex, involving the design of inputs to achieve desired outputs from LLMs. Jon Krohn discusses techniques like zero-shot and few-shot learning, which can optimize results but also consume valuable context window space 1. Prompt injection, where users manipulate prompts to extract sensitive information or disrupt functionality, poses additional risks. Safeguards are necessary to prevent such vulnerabilities, which could lead to intellectual property theft or data breaches 1.

Prompt injections can actually be quite dangerous.

--- Jon Krohn

Effective prompt engineering requires balancing creativity with security to protect against these threats 3.