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Plan the performance improvements in focused stages

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Eric Rakestraw
2026-10-09 00:02:21 +00:00
parent 481e2d494c
commit 3fde1f6cbb
2 changed files with 465 additions and 427 deletions
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docs/roadmap/performance.md
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@@ -10,9 +10,10 @@ preparation, rendering, and execution paths. Large payloads and concurrent
library use make redundant full-payload copies especially worth addressing.
Prefer small, idiomatic improvements over additional framework machinery.
This roadmap captures the intended outcomes, recommended changes, and suggested
order of work. It is not a detailed implementation plan or a new public
performance guarantee. Existing contracts remain owned by GoDoc, the
This roadmap captures the intended outcomes and recommended changes. The
[implementation plan](implementation.md) owns staged work and verification.
This roadmap introduces no new public performance guarantee. Existing contracts
remain owned by GoDoc, the
[format reference](../formats.md), and the
[integration contract](../integrations/openai-compatible-chat.md). Preserve the
boundaries in the [architecture policy](../policy/architecture.md) and
@@ -145,19 +146,6 @@ errors. The target is inspection cost driven by the information being inspected,
not unrelated provider payloads. This requires no new public lookup API and
does not move provenance analysis into ordinary prompt execution.
## Suggested Order Of Work
Start with basic validation and template output (items 2 and 3), then file-read
capacity (item 5). These are localized changes with directly identifiable
allocation costs. Follow with request encoding and identity-only inspection
(items 4 and 7), preserving their error and selection semantics.
Keep prepared-state consolidation (item 6) conditional on measured savings and
a clear ownership analysis; it has the greatest risk of weakening isolation or
concurrency behavior. The ConfigKit request (item 1) is complete and does not
block local improvements. This order is a recommendation, not a
dependency between otherwise independent changes.
## Benchmarking And Verification
Use focused, offline before/after benchmarks to establish useful savings in