Local LLMs & Ollama
Anticipate failure modes
Name failures by their mechanism in local LLMs on the offline clinical note assistant, not with a generic hallucination label.
1Learn the idea
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Response design
For each severe local LLMs failure on the offline clinical note assistant, define stop condition, safe state, owner, and lasting prevention. Rollback only works if prior prompts, indexes, and models remain available. “Send to a human” needs queue capacity and context—not just a button name.
Run one tabletop on the offline clinical note assistant for local LLMs: inject a defect, verify detection, contain, recover, and keep the blameless trace.
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Make it operational
After the tabletop, store the injected local LLMs defect for the offline clinical note assistant as a regression fixture. If the same failure later reaches users silently, your detection story was aspirational. Detection without a fixture tends to rot for local LLMs.
Also pin one numeric memory from this local LLMs chapter: A 7B 4-bit model may fit in ~5–6 GB RAM yet lose accuracy on rare clinical abbreviations—measure on your notes, not only tokens/sec. That number is not decoration; it is a template for how claims about local LLMs on the offline clinical note assistant should look in design docs. Scoped specifically to local LLMs / offline clinical note assistant / failure-modes.
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Common mix-ups
People confuse local LLMs with neighboring buzzwords when debugging the offline clinical note assistant. Before changing prompts, ask whether the broken stage was evidence gathering, the local LLMs judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried local LLMs and it failed”) that blocks the next team on the offline clinical note assistant. Scoped specifically to local LLMs / offline clinical note assistant / failure-modes.
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Rehearsal (local-llms/failure-modes)
Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to local llms rather than generic AI advice.
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Rehearsal (local-llms/failure-modes)
Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to local llms rather than generic AI advice.
Read
Rehearsal (local-llms/failure-modes)
Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to local llms rather than generic AI advice.
Go deeper
Before you start
Why this matters
Invent an incident for the offline clinical note assistant involving local LLMs. What earliest signal should fire before users complain?
Phantom privacy
Detect with logs still leave the device. Respond by audit telemetry paths.
Silent quality drop after quant
Detect with abbrev expansion errors. Respond by gold note suite per build.
VRAM OOM mid-note
Detect with long contexts. Respond by cap context; test max note length.
Stale weights
Detect with missed security advisory. Respond by update runbook + SBOM.
In the wild
See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.
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