Picture the scene: it’s 2am, you’ve been bouncing a translation between three different AI models for hours, each one critiquing the last one’s version, and you suddenly realize you have no idea which draft is even yours anymore. The final text is worse than what you started with. That’s exactly what happened to Reddit user u/Fluid-Pattern2521, and instead of just closing the laptop and going to bed, they built a tool out of the frustration.
Why It Matters 🧭
Here’s the twist that makes this more than just another AI wrapper: when the poster went back and reviewed their logged sessions, most of the “the model went off track” moments turned out to be them drifting first, with the model just following along. We talk a lot about prompting the model better, but almost nobody audits their own side of the conversation. This tool does both at once, on the same four dimensions, turn by turn.
Think about how a normal multi-model chain actually plays out. You paste something into model A. You copy its answer into model B and ask it to critique. You copy that critique into model C and ask for a rewrite. By the fourth or fifth hop, small word choices have quietly shifted meaning, tone has drifted from formal to casual (or the other way around), and nobody, human or model, flagged it because everyone was reacting to the last message instead of the original goal. That’s how you end up at 2am with a translation that’s technically been “improved” five times and reads worse than the first draft. The 3C+1E test exists because that failure mode is common and invisible while it’s happening.
How-To Steps 🛠️
The tool is called the 3C+1E Emphasis Test, and it’s refreshingly low-tech: one offline HTML file, no login, no API calls.
- Before you start your session, write down one “emphasis” sentence, the single thing that has to survive no matter how much the tone or topic shifts. For a translation job, that might be “preserve the original’s sarcasm.” For a technical rewrite, it might be “keep the exact API parameter names untouched.”
- Run your AI session as normal, whether that’s one model or a relay of three or four.
- Feed the transcript into the tool and it scores both the model’s responses and your own inputs on four dimensions: Clear, Compact, Coherent, plus your declared Emphasis.
- It flags where drift happened and who (or what) triggered it first, turn by turn, so you can see the exact message where things went sideways instead of guessing.
- Export the results as JSON or CSV so you can compare sessions across models and over time. That last part matters more than it sounds. Once you’ve run it on a handful of sessions, patterns show up. Maybe you always drift on the third exchange because you get impatient and start shorthand-ing your instructions. Maybe one particular model consistently loses the emphasis sentence by turn five. You can’t see that from a single session, you need the export to line them up.
Grab it here: doi.org/10.6084/m9.figshare.32320875
Tips & Tricks 💡
One detail buried in the comments is worth pulling out: the tool also checks whether you were actually qualified to judge that session’s output, factoring in language, domain, and tone. The poster admitted this mattered more than they expected once they started tracking it. If you’re auditing a French-to-English translation and your French is rusty, the tool flags that your own judgment calls in that session carry less weight, which is a humbling thing to see in writing but genuinely useful.
A few practical ways to get more out of it: run the test on your worst session first, not your best one, since that’s where the drift patterns are most obvious and easiest to learn from. Keep your emphasis sentence short and concrete rather than abstract (“keep it under 200 words” beats “keep it punchy”), because vague emphasis statements are hard for the tool (and you) to score against. And if you’re chaining models, log the full transcript as you go rather than trying to reconstruct it afterward. Half the value here comes from having the raw turn-by-turn record, not a summary of what you remember happening. Worth trying on your next multi-model chain before you assume the AI is the one losing the thread.
Call to Action ⚓
One commenter pushed back with a fair point: you’re still the one grading your own drift, and that’s the same judgment that drifted in the first place. The poster’s response was basically “fair, tear it apart.” That’s a real limitation and worth sitting with, since no self-audit tool fully escapes the bias of the person running it. But even a flawed mirror beats no mirror at all when you’re five models deep into a rewrite at 2am and can’t tell up from down anymore. So if you’ve ever lost a session at 2am and blamed the model, go download this thing and find out who was really steering.
Frequently Asked Questions
Q: If I’m the one scoring my own drift, won’t I have the same bias that caused the drift in the first place?
That’s a legit concern. Here’s how the tool sidesteps it: you enter your 3C+1E scores first, before seeing any results. Then the tool runs its lexical analysis (word counts, keyword matching against your emphasis) in the background. When your human judgment and the automated analysis disagree, which happens more often than you’d expect, that divergence is the actual insight. It shows where your subjective read differs from objective patterns, which tells you something real about your session.
Q: What’s the difference between my scoring and the tool’s automated analysis?
You’re scoring based on your read: Does this stay clear? Compact? Does it honor what I said my emphasis was? The lexical heuristic scores based on patterns, word density, keyword alignment, structural consistency. When they line up, great. When they diverge, it’s a signal: either you caught something the patterns missed, or the patterns spotted drift you didn’t consciously notice.
Q: Should I use this instead of having someone else review my sessions?
No, external review is still better for catching bias. A fresh reader spots assumptions both you and the model stopped questioning mid-session. But this tool works offline and alone, for real-time iteration. Think of it as a solo mirror, not a replacement for peer feedback. Use both: run this tool to audit yourself, then have someone else review the final output.
Q: Can the tool tell me if I’m actually qualified to judge the session?
Yes. It tracks whether you were qualified to judge that session’s output (language, domain expertise, tone). This turned out to matter more than the author expected. So if you’re evaluating a medical translation and you’re not a doctor, the tool flags it, a reminder that you might not be the right judge for that one.
Built a tool to audit both the model AND myself in long AI sessions (free, offline, single HTML file)
by u/Fluid-Pattern2521 in PromptEngineering