We’ve all been there—mid-project, making steady progress, and then BAM! The dreaded context window limit. Suddenly, your AI is back to square one, forgetting everything you’ve already painstakingly explained. Tools, processes, the project’s structure—gone. And what do you do? You start over. AGAIN. You explain the same tools, the same conventions, and the same context over and over. It’s like a never-ending loop of re-education.
Update, our fellow researchers at unwindai have posted an article on a similar topic.
THE AGONY OF REPEATING YOURSELF
Here’s the real kicker: it’s not just that the AI forgets what you told it. It forgets everything that’s related to the task at hand:
- The tools you’re using
- The files you’re working with
- The specific prompts that were effective
This results in wasted time and frustration—especially when you’re working on complex tasks. The process of repeating every detail feels like you’re trapped in a cycle of Groundhog Day, except the task isn’t getting any easier.
SO, WHAT IF THERE’S A BETTER WAY?
That’s where a system like Clood comes into play. Clood doesn’t just track your code; it tracks the context of your work. Instead of re-explaining every detail each time you start a new context window, Clood records:
- Which files you’re working on
- Which prompts worked
- The tools that helped you get there
And it does all of this within your git commits. That’s right—your git logs aren’t just tracking code anymore. They’re tracking your AI’s education, so when you revisit a task, the AI can pick up right where it left off.
THE TASK SYSTEM – BREAKING DOWN THE WORK
But we’re not just tracking files and prompts. We’re talking about an entire task management system that organizes your work into:
- Tasks
- Subtasks
- Plans
- Tools
- Affinities
This hierarchical structure means you can see at a glance what needs to be done and the most effective way to approach it. Plus, each task and subtask has its own affinity—a record of what’s worked well in the past. Did a specific tool or approach work well last time? You’ll know it.
THE AFFINITY SYSTEM – LEARNING FROM THE PAST
The magic of this affinity system is its ability to adapt. As you work on more tasks, it tracks what works for each one, associating the right prompts, tools, and files with the task at hand. This means when you revisit a task or subtask, the AI can infer what’s most likely to succeed based on what’s worked in the past. And because it’s not rigid, you can update this system as new methods and tools emerge.
It’s like having an AI that learns from your past, not just your input.
NO MORE STARTING OVER – LET’S MOVE FORWARD!
The bottom line is simple: Stop wasting your time re-explaining everything to your AI. By leveraging tools like Clood, with its context-aware system and task hierarchy, you can ensure that your work keeps moving forward without the need for constant resets. Your AI can remember what’s important and learn from your progress, instead of starting from scratch every time.
Because, let’s face it, you’ve got more important things to do than explain why npx --yes matters for the hundredth time.
A word from our editors. That was Frank, our old army buddy who worked on the cray systems in the 80s. He’ll be contributing articles but like frank, he often forgets the meat (vegan) so we as a note:
Large language models (LLMs) suffer from a critical limitation: context loss. They frequently forget task details—tools used, relevant files, even previously successful prompts—leading to wasted time and frustration, particularly on complex projects. Simply providing supplementary documentation, like Markdown files, isn’t a reliable solution; LLMs often fail to integrate this information effectively.
This shortcoming stems from the inherent nature of LLMs. While providing initial instructions can improve efficiency, this benefit is fleeting. The model’s grasp of the context remains tenuous, easily overwhelmed by its limited context window.
However, a key observation emerges: LLMs exhibit significantly improved performance when directly processing raw data—code, spreadsheets, images—rather than relying solely on descriptive instructions. This direct access fosters a deeper, more meaningful engagement with the information, enhancing working memory and reducing context loss. The model appears to derive a more robust understanding of patterns and relationships from the data itself than from secondary descriptions.
This highlights a necessary shift in workflow. While initial instructions remain valuable, they should not be the primary reliance. Optimal LLM utilization involves maximizing direct data access alongside concise, high-level guidance. This allows the model to learn actively from the data, creating a more stable understanding of the project. Tools enabling continuous data access and the ability to reference prior interactions, potentially through integration with specialized data management systems, are crucial for breaking the cycle of repetitive prompting and maximizing efficiency.
