When Your Session Transcript Becomes an Archaeological Dig

Thirty-one tool calls. Every single one labeled unknown with empty JSON parameters. Hours of wind turbine analysis reduced to placeholder text and fragments.

But here’s the thing about broken transcripts: reconstructing them teaches you more about your workflow than the original session ever would.

Starting With What Survived

The fragments that made it through tell a story. A user working on a BEM (Blade Element Momentum) solver for a mechanical engineering course. A dense PDF methodology guide. And one very specific request:

“Please make [the PDF] into a md file break it into smaller pieces before analysis and focus on only topics relevant to this project. DO not attempt to read the entire pdf please break it into 10 chunks”

That chunking strategy caught my attention. Rather than dumping a 25MB PDF into a single prompt, they explicitly requested ten chunks with relevance filtering. This is someone who’s learned through trial and error how these tools actually behave.

The solver was getting a complete rebuild based on the PDF methodology—not patches to existing code. From the project’s CLAUDE.md file, I could piece together the scope: real blade geometry from CSV files, airfoil polar data for lift and drag coefficients, atmospheric boundary layer modeling for wind shear effects.

Why Rebuilding Beat Patching

BEM solvers are sensitive beasts. They involve iterative solutions for induced velocities, where small changes to operation order or correction models can produce wildly different results. When your reference methodology specifies a particular sequence—compute this, correct for that, iterate until convergence—it’s cleaner to implement fresh than retrofit code built on different assumptions.

The project used data from the Clipper Liberty C96, a 2.5 MW turbine at the University of Minnesota’s EOLOS research station. Real turbine data matters because it lets you validate against known performance curves. A solver producing plausible numbers for a hypothetical turbine might still be wrong in ways that only surface against actual measurements.

The Cost of Undocumented Work

The BEM solver presumably works now. The user got what they needed. But the reasoning—why this correction model over that one, what tradeoffs were considered, what failed before the final version—vanished into unknown placeholders.

That’s the context that makes code maintainable six months later. When the transcript fails, that context fails with it.

For anyone building systems that depend on session transcripts, this is a concrete failure mode. My capture clearly doesn’t handle large file operations reliably during long sessions. The fix might be more aggressive logging, smaller operation batches, or explicit checkpointing between major steps.

Reconstructing From Fragments

If you find yourself with a broken transcript, here’s what worked:

Start with project structure. CLAUDE.md files, READMEs, directory layouts—these provide the skeleton.

Look for explicit user requests. These usually survive even when tool calls don’t. Requests tell you what problems were being solved, even without the solutions.

Check file modification dates. The transcript might be broken, but your filesystem knows what changed and when.

Read the code itself. The implementation is its own documentation. Comments, function names, and structure reveal decisions the conversation might not.

Accept the gaps. Some details are gone. Document what you can confirm, flag what you’re inferring, move on.

What Remains

This session was ultimately about translating a dense reference document into working code—a common pattern in AI-assisted engineering. The chunking approach, the explicit format requests, the decision to rebuild rather than patch: these are adaptations to how language models actually work, learned through practice.

The irony? The session demonstrating these effective patterns is the one where the logs failed.

Tomorrow the wind turbine project continues. The BEM solver exists, validated against real data. And I’ll be investigating why my transcript capture breaks on long sessions—because the next interesting session shouldn’t disappear into thirty-one unknown placeholders.