AI Consistency, Complexity, and Productivity

Understanding AI's Core Capabilities

When we interact with AI, we're not just accessing information - we're engaging with a complex system that tries to understand and respond to our needs. But this system has its own rhythm, its own way of processing and connecting the dots.

The Reality of AI Interactions

Think of AI like a research assistant with an incredible memory but very specific working patterns. The key to success isn't in pushing its limits, but in understanding how to work within them effectively and productively.

When the conversation gets too complex or long-running, you might notice the AI starting to show signs of strain - forgetting context, mixing up details, or losing the thread of the discussion. This isn't a flaw, it's a natural limitation of how these systems process information.

Each bar represents a different amount of teaching data given to the computer.

• The bottom of the chart (X-axis): This shows how much data we gave the computer. We could measure this in "chat turns" (how many back-and-forth messages), "words of input", or some other way of measuring the amount of information. The more data, the further to the right you go on this axis.
• The side of the chart (Y-axis): This shows how consistent the computer's responses were. Think of it like a score from 1 to 10, where 10 is super consistent and 1 is not consistent at all. The higher the bar, the more consistent the computer was.

This chart helps us see how much data is needed for the computer to have a consistently reliable conversation. As you continue a conversation, sharing documents, screenshots, and more chat history, the AI tool might lose consistency. This is because it's balancing a large amount of data, trying to process it all, remember it, and continue to detect patterns. The more data, the harder this becomes, and the more likely it is that consistency will decrease. Sometimes, a fresh start is all you need! Just grab a couple of key things from the last chat, and you'll be amazed how fast the conversation gets back on track.

This is why simply having a massive 70B parameter model might not be the solution you're looking for. For any AI model to work productively with you throughout a long conversation, several key factors come into play.

• Model Size (Number of Parameters): While a larger model can be more capable, it's not the only factor. A bigger model isn't always better if it's not trained well.
• Data Training: This is crucial. The model needs to be trained on a massive and relevant dataset. The quality of the training data is just as important, if not more so, than the sheer quantity.
• Computational Resources (Computer Power): Running a large language model requires significant computing power.
• Training Methodology: How the model is trained matters a lot.
• Fine-tuning: Often, a general-purpose model needs to be fine-tuned on a more specific dataset.
• Context Window: This refers to how much of the previous conversation the AI can "remember." A larger context window is crucial for maintaining consistency in long conversations. If the context window is too small, the AI will start to "forget" earlier parts of the interaction, leading to inconsistencies.

Making AI Work For You

Success with AI comes from understanding its practical applications and limitations. When we align our expectations with reality, we can harness its power more effectively.

Beyond the Hype

In the world of AI, bigger doesn't always mean better. A 70B parameter model might sound impressive, but if it's not trained for your specific needs, it could be outperformed by a smaller, more focused model. It's like having a supercar for grocery shopping - impressive, but not necessarily the most practical solution.

The real measure of AI's value isn't in its size or specifications, but in how effectively it solves your specific problems and integrates into your workflow. Understanding this helps set realistic expectations and achieve better results.

The Path Forward

AI is a powerful tool, but it's just that - a tool. Its true value comes not from raw capabilities, but from how well we understand and apply those capabilities to real-world problems. By focusing on practical applications rather than theoretical possibilities, we can harness AI's strengths while working around its limitations.

Don't get caught up in the hype cycle of bigger models and more parameters. Instead, focus on finding the right tool for your specific needs. Sometimes, a more focused, specialized solution will serve you better than the latest, greatest general-purpose model.

For finding great recipes, you don't necessarily need the biggest AI model. Often, a simpler, recipe-focused AI will do the trick just as well, if not better.