Claude Architect · Pelajaran

Risiko Perangkuman Progresif

Angka, persentase, dan tanggal menjadi samar saat diringkas.

Pelajaran 2 dari 413 langkah

Risiko Perangkuman Progresif adalah pelajaran Claude Architect gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Claude Architect, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Claude Architect mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

Why Summaries Drift

Long-running agents can't keep every turn in the context window. A common fix is progressive summarization: periodically compress earlier messages into a shorter recap so the conversation keeps fitting.

It works for narrative flow. But it has a sharp failure mode for the Context Management & Reliability domain: when prose gets compressed, numbers, percentages and dates go vague. "$1,284.50 refunded on 2026-03-14" quietly becomes "a refund was issued recently."

What Actually Gets Lost

Summarization optimizes for gist, not precision. The model keeps the storyline and drops the exact tokens that carry transactional meaning.

  • Amounts: "$4,999.00" → "a large charge"
  • Percentages: "a 17.5% discount" → "a discount"
  • Dates: "order placed 2026-01-09" → "earlier this year"
  • IDs: "order #A-88231" → "the order"

These are exactly the facts downstream tools and policy checks depend on.

/compact Carries the Same Risk

In Claude Code, /compact compresses the running context to free up room — the same mechanism, the same risk. After a compaction, a stack trace's line numbers, a failing test's exact assertion, or a config value can come back fuzzy.

The fix isn't to avoid compaction; it's to make sure the precise facts you'll need later don't live only inside the part that gets compressed.

# Claude Code session: compaction is convenient but lossy
/compact   # compresses earlier turns -> numbers/dates can become vague

# Safer: persist exact facts that must survive
/memory    # write durable facts into CLAUDE.md before compacting

The Core Pattern: A Case Facts Block

The exam-grade fix is simple: pull transactional facts into a separate "case facts" block kept verbatim, outside the summary.

Summarize the conversational narrative freely — but maintain a structured, append-only block of exact values that is never passed through the summarizer. The summary keeps context cheap; the case-facts block keeps the numbers exact.

Structuring Case Facts

Keep the block machine-readable and verbatim. Each entry preserves the original value exactly as stated, with enough labeling to reuse it later.

This block is re-injected into the system prompt on every turn — remember the API keeps no server-side state, so you resend it as part of the full message history each request.

case_facts = {
    "customer_id": "CUS-44190",
    "order_id": "A-88231",
    "order_date": "2026-01-09",
    "order_total": "4999.00",     # exact, never rounded
    "discount_pct": "17.5",
    "refund_amount": "1284.50",
    "refund_date": "2026-03-14",
}

system = (
    "You are a support agent.\n"
    "## CASE FACTS (verbatim, authoritative)\n"
    f"{json.dumps(case_facts, indent=2)}\n"
    "## CONVERSATION SUMMARY (may be lossy)\n"
    f"{rolling_summary}"
)

Summarize Narrative, Preserve Facts

Split your context-management routine in two. When you compress, run the summarizer only over the dialogue, and leave the case-facts block untouched.

Order matters too: facts go near the top, the lossy summary lower down. The model attends to the start and end of context more than the middle (lost-in-the-middle), so authoritative numbers belong where attention is strongest.

def build_context(history, case_facts, max_dialogue_turns=8):
    recent = history[-max_dialogue_turns:]
    older = history[:-max_dialogue_turns]
    # Compress ONLY the narrative; facts are excluded from summarization
    summary = summarize(older) if older else ""
    return {
        "facts_block": case_facts,   # verbatim, top of context
        "summary": summary,          # lossy, fine for narrative
        "recent": recent,
    }

Extract Facts at Capture Time

Don't try to recover exact numbers after they've been summarized away — by then the source is gone. Capture them the moment they appear, straight from tool results.

Trim verbose tool output to the relevant fields before it enters history, but route the precise values you'll need into the case-facts block first. Use structured output (a tool call with a JSON Schema) so extraction is reliable, not a regex guess.

# After lookup_order returns, capture facts verbatim before trimming
order = tool_result["order"]
case_facts.update({
    "order_id": order["id"],
    "order_total": order["total"],     # keep raw string/decimal
    "order_date": order["created_at"],
})
# Now the full verbose payload can be trimmed/summarized safely

Why Retry Won't Save You

A tempting reflex: if a later answer cites the wrong amount, just retry with feedback. But retry-with-feedback only fixes format, structural or arithmetic errors — you resend the original doc, the wrong output, and the exact validation error.

Once a number has been summarized out of the context entirely, the information is simply absent from the source. Retry does not help when info is absent. Prevention (the facts block) is the only reliable fix.

Guard Hard Rules with Hooks, Not Memory

When a number gates a consequential action — say a refund threshold — never let a summarized, fuzzy amount drive the decision. Enforce it deterministically.

A PostToolUse or outgoing-call hook reads the verbatim case-facts value and blocks policy violations 100% of the time. Prompts are ~90% probabilistic; hooks are deterministic. Use hooks when failure has financial, legal or safety consequences.

{
  "hooks": {
    "PreToolUse": [{
      "matcher": "process_refund",
      "command": "check_refund_limit.py"
    }]
  }
}
# check_refund_limit.py reads case_facts.refund_amount (verbatim),
# not the lossy summary, and exits non-zero if > $500.

Keep Provenance Alongside Facts

For research and extraction work, a verbatim fact is more trustworthy with its provenance: the source URL or doc name, the exact quote, and the publication date.

Dates frequently resolve apparent contradictions between conflicting stats — "revenue was $2.1M" vs "$2.4M" often just reflects different quarters. Annotate conflicting numbers with their sources rather than letting a summary silently pick one and blur the rest.

fact = {
    "claim": "Q4 revenue was 2.4M USD",
    "value": "2400000",
    "source_doc": "FY26-Q4-earnings.pdf",
    "quote": "Total revenue for Q4 reached $2.4M.",
    "published": "2026-02-02",
}

Fresh Session vs --resume

When a long session has been compacted many times, a resumed thread may carry a degraded summary — and resumed tool results can be stale if the codebase changed. Sometimes a fresh session seeded with a structured summary (your clean case-facts block) beats --resume.

The structured block is the carrier: it lets you start clean without losing the exact numbers and dates the work depends on.

# Long thread got fuzzy after repeated /compact?
# Start fresh and hand over the verbatim facts block:
claude -p "Resume task. ## CASE FACTS (authoritative):
$(cat case_facts.json)"

# vs. claude --resume my-session  (may carry a degraded summary
# and stale tool results)

Quick Check: Protecting the Numbers

A customer-support agent runs for many turns and periodically summarizes earlier messages to stay within the context window. QA finds it now quotes refund amounts and order dates that are slightly off or vague. Which fix is exam-correct?

Recap: Keep the Facts Verbatim

Key takeaways for the Context Management & Reliability domain:

  • Progressive summarization (and /compact) makes numbers, percentages and dates vague — the gist survives, the precision doesn't.
  • Fix: maintain a separate case-facts block, verbatim, outside the summary; summarize only the narrative.
  • Capture facts at the source from tool results; never try to recover them after they're gone — retry can't restore absent info.
  • Place facts near the start of context (beat lost-in-the-middle); keep provenance + dates for research.
  • Gate consequential thresholds with deterministic hooks reading verbatim values, and prefer a fresh session with a structured summary over a degraded resume.
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Pertanyaan yang Sering Diajukan

Apakah pelajaran “Risiko Perangkuman Progresif” gratis?

Ya — teks lengkap “Risiko Perangkuman Progresif” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Claude Architect, upgrade ke CoddyKit PRO. Kursus Claude Architect mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Risiko Perangkuman Progresif”?

Angka, persentase, dan tanggal menjadi samar saat diringkas. Kamu berlatih Claude Architect dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai Claude Architect?

Tidak diperlukan pengalaman sebelumnya. Claude Architect di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Risiko Perangkuman Progresif” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran Claude Architect ini?

Ya. Setiap pelajaran Claude Architect menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Seluruh Riwayat Diperlukan
  2. Risiko Perangkuman Progresif
  3. Efek Hilang di Tengah
  4. Blok Fakta Kasus dan Pemangkasan Keluaran
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