I gave an LLM 2,000 Excel rows to analyze. Every second or third sentiment label was wrong.

The problem wasn’t necessarily the model.

The problem was the workflow.

LLMs are designed to take instructions and context, recognize patterns, and generate the most useful response they can. But the quality of that response depends heavily on the quality and clarity of the input.

A clear prompt helps.

A structured workflow helps even more.

When I first tested sentiment analysis on approximately 2,000 Excel rows in a single task, the output looked convincing. But during manual validation, I noticed that every second or third classification was inaccurate.

The model had too much data, too many decisions, and too little room for proper validation.

That is where LLM chaining becomes useful.

Instead of asking the model to complete one large task at once, the process is divided into smaller connected steps:

1. Clean and prepare the data

2. Analyze smaller batches

3. Classify sentiment

4. Identify recurring themes

5. Validate uncertain results

6. Combine the outputs into a final report

Each output becomes the input for the next step.

This reduces task complexity, makes errors easier to detect, and allows every stage to be checked before moving forward.

It can also prevent unnecessary context overload. Larger inputs consume more tokens, increase costs, and may push important instructions or data out of the model’s effective context. Official model documentation confirms that inputs and outputs consume tokens and that exceeding a model’s context window can cause requests to fail or older context to be truncated.

The lesson was simple:

Don’t give AI one enormous task and hope for a perfect answer. Build a process in which every step improves the next one.

That is the difference between using an LLM as a chatbot and using it as part of a reliable workflow.