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design/cost-reduction.md ADDED
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+ # Beacon — LLM Cost Reduction
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+
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+ ## Why This Exists
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+
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+ Beacon is in early stage. Every session hits the Anthropic API multiple times across three agents. Without guardrails the per-session cost is high enough to become a problem at even modest usage. This document captures the measured baseline, the ranked reduction options, and the implementation approach for each.
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+
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+ ---
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+
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+ ## Baseline (measured 2026-05-28)
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+
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+ ### Model assignments
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+
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+ | Agent | Model | MTok input price | MTok output price |
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+ |---|---|---|---|
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+ | Intake | claude-sonnet-4-6 | $3 | $15 |
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+ | Research | **claude-opus-4-7** | **$15** | **$75** |
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+ | Eligibility (parse + assess) | claude-sonnet-4-6 | $3 | $15 |
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+
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+ ### Token estimate per session
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+
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+ | Agent | ~Input tokens | ~Output tokens | Notes |
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+ |---|---|---|---|
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+ | Intake | 9,900 | 750 | System prompt (~905 tokens) × 5 turns, no caching |
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+ | Research | 9,200 | 1,500 | Opus model; trial JSON in tool results |
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+ | Eligibility | 3,300 | 400 | Bulk parse top-5 trials |
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+ | **Total** | **~22,400** | **~2,650** | |
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+
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+ ### Estimated cost
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+
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+ | Component | Cost/session |
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+ |---|---|
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+ | Research on Opus (input + output) | ~$0.25 |
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+ | Intake + eligibility on Sonnet | ~$0.10 |
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+ | **Total** | **~$0.35/session** |
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+
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+ Opus alone accounts for ~70% of the bill despite being used for only one agent.
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+
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+ ---
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+
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+ ## Reduction Options (ranked)
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+
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+ ### 1. Switch research from Opus → Haiku `HIGH`
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+
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+ **File:** `config.py`
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+ ```python
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+ # Before
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+ RESEARCH_MODEL = "claude-opus-4-7"
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+
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+ # After
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+ RESEARCH_MODEL = "claude-haiku-4-5-20251001"
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+ ```
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+
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+ Haiku costs $0.80/MTok input and $4/MTok output — roughly **19× cheaper than Opus** on input, **19× on output**.
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+
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+ The research task is: call a search tool, receive structured JSON, write a plain-English summary. It does not require Opus-level reasoning. Start with Haiku; step up to Sonnet only if output quality is noticeably degraded.
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+
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+ **Estimated saving: ~$0.22/session**
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+
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+ ---
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+
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+ ### 2. Add prompt caching to all three agents `HIGH`
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+
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+ The system prompt and tool schemas are identical on every turn within a session — perfect cache candidates. Anthropic's prompt caching discounts cached input tokens by **90%** with a 5-minute TTL.
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+
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+ **How to apply:** Add `"cache_control": {"type": "ephemeral"}` to the last static content block before the dynamic message history begins. For all three agents this means marking the system prompt string.
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+
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+ Using the `anthropic` SDK the pattern is:
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+ ```python
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+ client.messages.create(
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+ model=MODEL,
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+ system=[
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+ {
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+ "type": "text",
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+ "text": SYSTEM_PROMPT,
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+ "cache_control": {"type": "ephemeral"},
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+ }
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+ ],
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+ ...
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+ )
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+ ```
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+
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+ For intake specifically, the tool list (`INTAKE_TOOLS`) should also be marked cacheable since it never changes mid-session.
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+
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+ **What gets cached per agent:**
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+
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+ | Agent | Cacheable tokens | Turns | Tokens saved |
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+ |---|---|---|---|
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+ | Intake | system (~905) + tools (~637) | 5 | ~7,700 |
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+ | Research | system (~928) + tools (~273) | 2 | ~2,400 |
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+ | Eligibility | system (~200 each call) | 2 | ~300 |
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+
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+ **Estimated saving: ~$0.06/session** (on top of item 1, after switching research to Haiku)
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+
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+ > Note: Cached tokens are billed at 10% of the normal input rate. The cache write (first turn) is billed at 125% — break-even is the second turn.
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+
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+ ---
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+
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+ ### 3. Trim the intake system prompt `MEDIUM`
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+
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+ **File:** `prompts.py` — `INTAKE_SYSTEM` (~905 tokens today)
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+
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+ The prompt contains verbose per-phase descriptions of Phase 0 through Phase 4, EAP, and observational studies written to help the LLM explain them to the patient. Most of this guidance text can be reduced to a compact reference table without changing behavior.
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+
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+ Target: **≤ 450 tokens**.
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+
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+ Areas to cut:
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+ - Phase explanations: keep the phase names and one-line descriptions, remove the multi-sentence patient-facing explanations (the LLM will generate appropriate phrasing anyway)
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+ - EAP / observational descriptions: one sentence each is enough
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+ - Repeated instructions already implied by tool schemas (e.g., "use submit_profile when you have all required fields" — the tool definition already enforces this)
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+
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+ **Estimated saving: ~$0.01–0.02/session** (more significant with caching, since a smaller prompt fills cache faster)
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+
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+ ---
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+
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+ ### 4. Strip `summary` from trial data before LLM synthesis `MEDIUM`
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+
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+ **File:** `agents/eligibility.py` — `_FIELDS_TO_STRIP_AFTER_PARSE`
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+
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+ `bulk_parse_and_strip` already removes `eligibility`, `std_ages`, and `healthy_volunteers`. The `summary` field (the trial's brief description, ~100 tokens each) is passed to the research LLM but it adds limited value once `parsed_criteria` and `deterministic_verdicts` are present.
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+
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+ ```python
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+ # Add to existing strip set
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+ _FIELDS_TO_STRIP_AFTER_PARSE = {
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+ "eligibility", "std_ages", "healthy_volunteers",
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+ "summary", # ~100 tokens × 5 trials = ~500 tokens saved
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+ "keywords", # rarely used in synthesis
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+ }
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+ ```
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+
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+ Keep `summary` in the data returned to the UI if needed for display.
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+
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+ **Estimated saving: ~500 tokens/session**
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+
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+ ---
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+
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+ ### 5. Reduce `max_tokens` for research output `MEDIUM`
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+
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+ **File:** `agents/research.py`
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+
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+ ```python
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+ # Before
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+ max_tokens=8096
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+
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+ # After
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+ max_tokens=2048
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+ ```
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+
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+ `max_tokens` does not directly cost money — only generated tokens are billed. But a tighter ceiling combined with a "be concise" addition to `RESEARCH_SYSTEM` nudges the model toward shorter responses. Most research summaries are well under 1,000 output tokens; 8,096 is permissive enough that verbose models use it.
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+
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+ **Estimated saving: variable, likely 200–500 output tokens/session**
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+
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+ ---
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+
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+ ### 6. Ask 2–3 intake questions per turn `MEDIUM`
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+
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+ **File:** `prompts.py` — `INTAKE_SYSTEM`
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+
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+ Currently Beacon asks one question at a time. The intake typically takes 5–6 turns. A small instruction change to batch related questions (e.g., "ask for onset date and diagnosis date in the same message once the patient has named their disease") could cut 1–2 turns from the average session.
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+
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+ Each saved turn eliminates one full LLM call (~1,600 input tokens + ~150 output tokens).
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+
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+ **Estimated saving: ~$0.005/session per turn eliminated**
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+
164
+ ---
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+
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+ ### 7. Gate eligibility on distance `LOW`
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+
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+ **File:** `agents/eligibility.py` — `bulk_parse_and_strip`
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+
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+ `top_n=5` always processes the five closest trials. If trials 4–5 are beyond a patient's stated radius (e.g., >150 miles when radius_miles=100) they are unlikely to enroll. Skipping eligibility parsing for distant trials saves one LLM call's worth of tokens.
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+
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+ ```python
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+ to_parse = [
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+ t for t in trials[:top_n]
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+ if t.get("eligibility", "").strip()
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+ and (t.get("closest_site_miles") or 0) <= patient.radius_miles * 1.5
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+ ]
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+ ```
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+
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+ **Estimated saving: ~300–600 tokens when far trials are present**
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+
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+ ---
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+
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+ ## Projected cost after all changes
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+
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+ | Scenario | Est. cost/session |
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+ |---|---|
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+ | Today (baseline) | ~$0.35 |
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+ | After items 1 + 2 (Haiku + caching) | ~$0.05 |
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+ | After all items | ~$0.02–0.03 |
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+
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+ Items 1 and 2 alone achieve a **~85% cost reduction**. The remaining items are incremental polish.
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+
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+ ---
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+
196
+ ## Implementation order
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+
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+ 1. `config.py` — switch `RESEARCH_MODEL` to Haiku (5 min, immediate 70% saving)
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+ 2. All three agents — add `cache_control` to system prompt (30 min)
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+ 3. `prompts.py` — trim `INTAKE_SYSTEM` (1–2 hours, requires prompt quality check)
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+ 4. `agents/eligibility.py` — add `summary` / `keywords` to strip set (10 min)
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+ 5. `agents/research.py` — reduce `max_tokens` to 2048 (2 min)
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+ 6. Remaining items as time permits
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1832
  [[package]]
1833
  name = "tomlkit"
1834
  version = "0.14.0"