# RB-10 MAJOR: LLM prompts not preflighted against context window

**Severity:** MAJOR
**Path:** `crates/llm/src/lib.rs:143-166`, `:317-321`
**Source:** [2026-04-22 code review](../code-review-2026-04-22.md)
**Labels:** release-blocker, major, llm
**Status:** RESOLVED (2026-04-22)

## Resolution

`LlmEngine::generate` still tokenises the whole prompt up front, but it
now runs a dedicated prompt-budget preflight before creating the llama
context. The chosen behaviour is an early typed failure rather than
silent truncation:

- If `prompt_tokens + max_tokens + 64 reserve tokens` exceeds the
  8192-token cap, generation returns
  `EngineError::PromptTooLong { prompt_tokens, max_tokens, available_prompt_tokens, context_window }`.
- Prompts that fit exactly within the available budget still proceed and
  allocate an 8192-token context as before.

Regression tests:

- `prompt_preflight_rejects_oversized_prompt_tokens`
- `prompt_preflight_keeps_prompts_within_budget`

## Problem

`generate` tokenises and batches the full prompt at runtime. `context_window_size` hard-caps context at 8192 tokens. Long transcripts (a 30-minute dictation session is easily 4000–6000 tokens after segment joining) reach inference with prompts already bigger than the available context — causing late runtime failure instead of a controlled early-exit path.

## Acceptance

- Before inference begins, the prompt token count is compared against the available context window (minus the expected response budget).
- Oversized prompts either (a) surface a typed error the caller can handle gracefully, or (b) are truncated with a logged warning — decide during the fix.
- Regression test: synthesise a transcript whose tokenised form exceeds 8192 tokens, assert the chosen behaviour (early error or truncated input).

## Fix scope

Medium. Tokeniser access is already on the LLM path; the check is cheap. Decision work is in what to do when a prompt is too long (fail hard vs truncate).

## Dependencies

- None — standalone fix.
