Reasoning Models
LLM reasoning refers to the ability of AI models to
logically process information, step-by-step break down complex problems using “chain-of-thought” and
evaluate more thoroughly to provide higher quality answers.
This is an improvement from traditional LLM retrieval and response.
If models can explain the thought process on how a particular result was derived, that builds trust.
Below are some of the benefits of reasoning models like OpenAI o1 or Gemini Advanced
- Solve Complex Problems: Reasoning helps in solving complex problems with high level of accuracy than before. Helpful in domains such as coding, math’s, strategy, logistics, planning and science.
- Planning : Reasoning models, allow you to create effective plans for accomplishing a goal, which tools to use, in what order and working within guardrails.
- Less Prompting : Reasoning models require less prompting and steering to produce meaningful results.
- Image Reasoning : Use a reasoning model to extract intelligence out of an image for downstream processing
The downside of reasoning models are:
The downside of this approach is the context length increases, as the model adds the reasoning steps to the output. This increases the cost and latency. There is a trade off to make between cost, latency and intelligence. Not all tasks require in-depth thinking. Reasoning models are mostly suited for complex tasks.