Having spent considerable time this week evaluating the updated ChatGPT interface for its potential in constructing system logic and data transformation rules, I was particularly interested in the new 'scratchpad' feature, formally called the 'workspace' or 'notebook' area. My primary test case involved a multi-step API integration problem that required calculating rate limits, batching intervals, and handling pagination with variable payload sizes—a scenario rife with conditional logic and arithmetic.
The premise of a persistent scratchpad is promising for complex, iterative calculations. In theory, it should allow you to maintain state, refine formulas step-by-step, and reference earlier results without regenerating the entire chain of thought. From my methodical testing, I've observed the following:
* **State Retention:** The scratchpad does maintain a form of state within a single session, allowing you to build upon previous entries. This is a significant improvement over the standard chat flow where you must re-paste or re-explain intermediate values.
* **Stepwise Refinement:** For a problem like determining optimal batch size given a dynamic API rate limit (e.g., 100 requests/minute with a variable cost-per-request), I could first calculate the raw throughput, then adjust for network latency, and finally incorporate a safety margin—all in separate, additive steps within the scratchpad.
* **Reduced Context Corruption:** In standard chat, asking for a slight adjustment to a complex formula often leads the model to rewrite the entire solution, sometimes introducing new errors. The scratchpad seems to mitigate this, as you can direct edits to a specific preceding step.
However, its utility is currently bounded. Here is a simplified example of the type of workflow I tested:
```python
# Scratchpad Entry 1: Define base constraints
requests_per_minute = 100
batch_api_cost = 3 # 1 batch call = 3 rate limit tokens
window_minutes = 5
# Scratchpad Entry 2: Calculate max batches per window
max_tokens = requests_per_minute * window_minutes
max_batches = max_tokens / batch_api_cost # ~166.67
# Scratchpad Entry 3: Apply 20% safety margin and floor
safe_batches = int(max_batches * 0.8) # 133
```
The major limitation is that the scratchpad is still just a text area with slightly privileged context. It lacks true computational execution. The model is still *describing* the math, not *performing* it. For absolute precision, you still must rely on its ability to generate correct code you can run elsewhere, or its own (sometimes faulty) arithmetic.
My preliminary conclusion is that the scratchpad is a valuable organizational tool for structuring complex, multi-variable problems—akin to having a dedicated project notes file. It helps keep the logic straight and reduces repetition. However, it does not fundamentally upgrade the model's mathematical reasoning or calculation accuracy; it merely provides a cleaner workspace for that reasoning to occur. For API and integration work involving complex scheduling or transformation math, it's a step forward in process, but not a magic bullet for correctness.
I'm curious if others have stress-tested this feature with symbolic math, nested conditional statements, or real-world data mapping formulas. Have you found its benefits to be substantial, or merely cosmetic?
- Mike
- Mike