Training Manual Generator Error Codes

Why Your Training Manual Generator Keeps Crashing on Export

Most people don't think about error codes until something breaks right before a deadline. I spent three weeks last year debugging a workflow where our training manual generator would consistently fail at step 47 of a 62-step process. It wasn't a critical system error — it was one of those silent, misleading codes that tells you nothing useful without digging into the logs. The error code you need to understand is TMG-ERR-7734. This one pops up when the generator tries to merge image assets with text blocks that have overlapping bounding boxes. The manual itself looks fine in the preview pane, but the export fails with a generic "Rendering conflict" message if you don't know what to look for. I found this by pulling the full JSON log from the temporary output folder instead of relying on the UI error popup, which only showed code TMG-ERR-7734 with zero context.

Training Manual Generator Error Codes You'll Actually Run Into

Here's the thing about these codes that nobody puts in the documentation: they're not always consistent across versions. I upgraded from build 4.2 to 4.5 and two previously working codes — TMG-ERR-2100 and TMG-ERR-3891 — stopped resolving the same way. The first one used to mean a font embedding failure. In 4.5 it shifted to a permission issue on the asset cache directory. You need to check your version against the code table every time you upgrade, or you'll waste hours chasing the wrong fix. The codes themselves break into three tiers. Tier 1 (codes in the 1000–1999 range) are input validation errors. These happen when the source material doesn't meet the structural requirements — missing headings, unsupported character encodings, or images that exceed the 5MB per-file limit. Tier 2 (2000–2999) are processing errors. Something went wrong during the generation phase, usually a resource conflict or a timeout. Tier 3 (4000–4999) are output errors. The manual generated fine but the export to your chosen format failed. I run into tier 2 errors most often. The one that cost me the most time was TMG-ERR-2418, which triggers when the generator hits a RAM ceiling during batch processing of more than 200 pages. The error message says "Memory allocation failed at step 14 of 200." The workaround is to split your source documents into chunks of 50–75 pages and run them as separate jobs. I learned this after watching the process pool get killed by the OS at exactly 128GB of RAM usage, which is the hard limit on our servers. There's no setting to increase it. You just chunk the work.

Another thing worth noting: some error codes are benign. TMG-ERR-1502 and TMG-ERR-1503 show up when you're using non-standard heading levels in your source documents, like H5 or H6. The generator will warn you but still produce a usable manual. If you see these in your output log, you can safely ignore them unless you actually need those heading levels preserved in the final document. For the actual lookup process, I keep a local spreadsheet that maps code ranges to their typical causes and fixes. When an error appears, I check the code against my spreadsheet first, then pull the log file from the temp directory if the spreadsheet doesn't have an entry. The logs are in /tmp/tmg-logs/ and they're structured as newline-delimited JSON. The error entry will have a stack_trace field that points directly to which module failed. Without that stack trace, you're just guessing at what went wrong. There's also a download at the vendor's support portal where they list known error codes for each version. It's not always up to date, and I've seen cases where they listed a code as resolved that was still broken in production. Cross-reference anything you find there with your own test run before you invest time in a fix.

What to Do When You Hit an Unknown Code

If you encounter an error code that isn't in any documented table, start by checking whether it's a transient issue. These generators do occasional hiccups — network timeouts during asset fetches, race conditions when multiple processes try to write to the same temp file. Run the job again with a smaller batch. If it succeeds, it was probably transient. If it fails consistently with the same code, then it's a real issue and you need to dig into the logs. I've also found that enabling verbose logging through the config file changes the error codes you see. By default the generator suppresses some internal warnings, but if you set log_level: verbose in the config, previously hidden errors surface before they become blocking failures. This alone cut my error rate in half because I could fix source document issues before they caused export failures downstream. The generator doesn't handle large teams sharing the same asset library well. When five people are pulling images from the same S3 bucket simultaneously, you'll start seeing TMG-ERR-2900-series errors related to asset locking conflicts. The fix is to set up individual asset libraries per team member or stagger the generation jobs so they don't overlap. This is a bottleneck that the vendor acknowledges but hasn't fully addressed in any release yet.

If your main concern is just getting past the common errors without spending hours in the logs, the quickest path is to validate your source documents before running them through the generator. A simple script that checks for image sizes under 5MB, heading depth between H1 and H4, and valid UTF-8 encoding will catch about 80% of the tier 1 errors before they ever reach the generator. I wrote one in Python that runs in about 30 seconds for a 100-page document set, and it's saved me more trouble than the generator's built-in validation ever did.