[CharlieDigital]

The use case we have is that we are asking the LLM to write articles.

As part of this, we tried having a reviewer agent "correct" the writer agent.

For example, in an article about a pasta-based recipe, the writer wrote a line like "grab your spoon and dig in" and then later wrote another line about "twirl your fork".

The reviewer agent is able to pick up this logical deviation and ask the writer to correct it. But given an instruction like "it doesn't have to be perfect", the reviewer will continue to find fault with the output from the writer for each revision so long as the content is long enough.

One workaround is that instead of fixing one long article, have the reviewer only look at small paragraphs or sections. The problem with this is that the final output can feel disjointed since the writer is no longer working with the full context of the article. This can lead to repeated sentence structure or even full on repeated phrases since you're no longer applying the sampling settings across the full text.

In the end, it was more efficient and deterministic to simply write two discrete passes: 1) writer writes the article and 2) another separate call to review and correct.