Observability, Token Budgets, and Guardrails

Posted 3 days ago by Dino Cajic

 

As AI features move from experiments to production, two things start to bite: cost drift and opaque failures. The fix is not “more dashboards.” It’s an operating model: instrument every step, enforce token budgets, design caches that won’t burn you, and make errors useful for both developers and users.

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1) Observe the whole flow

Make every request traceable from the first byte to the last token.

Minimum structured event per request

  • Correlation ID and user/tenant ID.
  • Model, version, parameters, tool list, temperature, top_p.
  • Prompt token count, completion token count, total tokens.
  • Estimated and actual cost.
  • Cache status (miss, exact hit, semantic hit, bypass).
  • Retrieval context details (document IDs, versions, chunk counts).
  • Latency by stage (retrieval, tool call, model, post‑process).
  • Outcome (success, degraded, failed) and error code if any.

Baseline dashboards

  • Tokens per request (p50/p95) by feature.
  • Cost per user and per endpoint, stacked by model.
  • Cache hit rate and TTL effectiveness.
  • Context length distribution and trim frequency.
  • Tool latency and error rate.

2) Treat tokens like money

If you don’t meter tokens, you’re budgeting blind.

Guardrails

  • Set per‑feature and per‑user monthly token budgets.
  • Warn at 80% and hard‑stop or degrade at 100%.
  • Track prompt vs. completion ratio; long prompts with short answers are a red flag.
  • Estimate cost before you call the model: est_cost = (prompt_tokens * in_price_per_token) + (completion_tokens * out_price_per_token)

Useful thresholds

  • Max prompt tokens per endpoint.
  • Max completion tokens per endpoint.
  • Max total tokens per request.
  • Daily token ceiling per tenant.

3) Cache and dedupe

Caching is the easiest way to cut cost and latency, until stale or unsafe hits create bad answers. Design it like a contract.

Exact‑match cache

  • Key on a canonical fingerprint: normalized prompt + model + params + tool list + retrieval IDs + tenant + locale.
  • TTL aligned to data freshness; short TTLs for fast‑changing sources.
  • Invalidate on model change, system prompt change, or knowledge base version change.

Semantic cache (optional, use carefully)

  • Key on an embedding of the normalized user question.
  • Require a high similarity threshold and re‑rank with a lightweight check.
  • Store the answer and its provenance so you can explain why a hit was served.
  • Never cross tenant boundaries. Include locale.

4) Trim context safely

More context is not always better. It’s often just more. Trim with rules so you keep meaning, not noise.

Rules of engagement

  • Never trim the system prompt or safety instructions.
  • Prioritize the last user turn and the assistant’s previous answer.
  • Use retrieval with scoring; take the top‑K that actually match the query.
  • Deduplicate near‑identical chunks before trimming.
  • Compress low‑value history into summaries with explicit length targets.
  • Reserve token “budgets” by segment. Example split: 20% system, 30% latest dialogue, 30% retrieved facts, 20% tools/results.

Simple trim order

  • Keep system instructions.
  • Keep current user message.
  • Keep last assistant turn.
  • Add top‑K retrieved chunks.
  • Add recent chat history until you hit the cap.
  • Summarize overflow and append a one‑line summary token.

Pseudo‑logic

budget = MAX_TOKENS - SAFETY_MARGIN
add(system)
add(latest_user)
add(last_assistant)
add(topK_retrieval)
while tokens < budget: add(previous_turns_newest_first)
if tokens > budget: summarize_tail_to_target()

5) Fail fast with meaningful error paths

A good error is specific, structured, and suggests the next action. A bad error is a 500 and a shrug emoji.

Common error codes

  • VALIDATION_ERROR
  • TOKEN_BUDGET_EXCEEDED
  • CONTEXT_TRIM_FAILURE
  • TOOL_TIMEOUT
  • PROVIDER_5XX
  • SAFETY_BLOCKED
  • KB_VERSION_MISMATCH

Error payload shape

{
  "error_code": "TOKEN_BUDGET_EXCEEDED",
  "message": "Request would exceed the 8k token cap for this endpoint.",
  "correlation_id": "a1b2c3",
  "feature": "support_reply",
  "suggested_action": "Reduce attachments or switch to 'concise' mode.",
  "limits": { "max_total_tokens": 8000, "observed": 9650 }
}

Degradation paths

  • Serve a cached answer with a “last updated” timestamp.
  • Return a concise summary instead of a long-form write‑up.
  • Skip non‑critical tools and tell the user what was skipped.
  • For provider outages, switch to a backup model with a clear banner.

6) Reference flow

Client
  → API Gateway
    → Orchestrator (correlation ID, budgets, routing)
      → Exact Cache -> Semantic Cache
      → Retrieval Service (versioned KB)
      → Guardrails (input checks, PII, safety)
      → LLM Call (with max tokens & stop sequences)
      → Post‑Process (lint, formatting, safety check)
      → Observability Sink (traces, logs, metrics)

7) What “good” looks like after 30 days

  • Token variance down 30–50% without hurting quality.
  • Cache hit rate above 35% for repeat intents.
  • p95 latency down due to trimmed context and fewer tool calls.
  • Error budgets visible by feature and tied to roadmap fixes.
  • Engineers can trace any bad answer in one click, end‑to‑end.

8) Implementation checklist

  • Add structured logs with token counts and cost to every request.
  • Stand up token budgets per feature and tenant.
  • Launch exact‑match caching with clear keys and TTLs.
  • Implement safe context trimming with a documented policy.
  • Define an error taxonomy and return JSON errors with next steps.
  • Build dashboards for tokens, costs, cache hits, latency, and failure rate.
  • Schedule weekly reviews to prune high‑cost prompts and tools.

AI features don’t become reliable by magic; they become reliable because you treat tokens, context, and errors as first‑class citizens. Observe everything. Spend intentionally. Trim with rules. Fail in ways that help you recover.

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