When we started Far Horizons, most teams approached large language model work the same way they approached classic SaaS features: collect requirements, draw up a sizeable PRD, disappear for a few sprints and hope the release matches expectations. That rhythm rarely survives contact with LLM reality.
Models change weekly, APIs evolve without notice, and human feedback tends to be qualitative rather than binary. Instead of fighting that, we leaned into a lab-inspired cadence focused on tiny experiments, fast decisions, and documented learning.
What Lab Notes are
Lab Notes are short write-ups that capture:
- The real problem statement the stakeholder brings us, not just the feature request.
- The constraints we put around an experiment (budget, latency, proprietary data, permissible hallucination rate).
- The instrumentation we rely on to know whether a prompt, agent, or orchestration layer is genuinely better than the baseline.
- The decision we took and the next question it unlocked.
Each post mixes architecture sketches, prompt fragments, evaluation snippets, and the operations playbooks that keep everything sane once the prototype leaves the lab.
Why share them
Two reasons:
- Repeatable patterns. The same three or four issues appear in every enterprise LLM conversation—context windows, data freshness, guardrails, and change management. Publishing the patterns we reach for speeds up every future deployment.
- Brutal honesty. Not every idea clears the bar. We include the discarded approaches so you can skip a week of wheel-spinning.
What’s coming next
The first batch of Lab Notes will cover:
- Evaluating retrieval strategies without labelling a million documents.
- Designing a lightweight agent that hands off to humans without losing state.
- Building confidence dashboards that product teams actually use.
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