TL;DR:
The LLM struggles with consistently understanding complex tasks and retaining context, making it difficult to maintain productive workflows. Context window limitations and session breaks hinder progress, even when using workarounds like session notes, which are time-consuming and imperfect. These challenges highlight the need for better tools to handle context retention and continuity.
I have encountered the same issue, and I believe it stems from two interconnected challenges: the LLM’s difficulty in consistently understanding complex instructions and the inherent limitations of the context window. When the LLM is performing well, there is a natural desire to sustain that productive collaboration, either by continuing the session later or picking it back up on another day. However, the inability to seamlessly continue a session—combined with the eventual degradation of context even during ongoing interactions—makes it difficult to maintain an effective workflow. These limitations are particularly pronounced when attempting intricate, multi-step tasks, and even workarounds like session notes come with their own set of challenges. Let me break this down further.
1. Difficulty in Establishing Context with the LLM
One of the main challenges is that the LLM does not always grasp complex explanations or instructions right away. It often requires multiple attempts to phrase things in a way the LLM can understand, which can feel like trial and error. This inconsistency makes it difficult to systematically progress through intricate workflows, as you frequently need to pause, adjust your inputs, and hope the LLM correctly interprets what you are asking. This trial-and-error process not only slows down the work but also disrupts the continuity and flow needed for tasks requiring precision and depth.
2. Limitations of the Context Window
Even when the LLM eventually understands the task and collaboration becomes productive, the limitations of the context window create another major obstacle. As the session progresses, earlier parts of the conversation inevitably get pushed out of memory, rendering the interaction less effective. For complex, multi-step workflows, this results in the loss of critical information that is essential for maintaining continuity. Ultimately, the session becomes fragmented and loses its usefulness, requiring frequent restarts or additional effort to reintroduce lost context.
3. Challenges of Maintaining Continuity through Session Notes
To mitigate these issues, I’ve tried maintaining parallel session notes to track what has been done and create a record that can be used to restart a session. While this approach helps to some degree, it is far from ideal. Maintaining detailed notes is time-consuming and requires significant manual effort. Furthermore, the LLM’s inability to retain critical details can still create gaps, making it difficult to fully recreate the context. For example, in one instance, the session appeared to break or split entirely, causing all prior context to be irretrievably lost. This makes it particularly challenging to sustain a systematic workflow over an extended session or series of related tasks.
In summary, the combination of inconsistent understanding, context window limitations, and the manual effort required to maintain continuity significantly hinders the effectiveness of the LLM for complex, multi-step workflows. These challenges highlight the need for more robust tools or features to better handle context retention and task continuity in these scenarios.
