Caching, Streaming, Fallbacks, and Graceful Degradation

Fast AI systems use caches and streaming, but both can violate correctness if they ignore identity, source versions, model versions, policy, or validation. Cache only at explicit boundaries with complete keys and deletion hooks. Stream provisional text only under a UX contract, preserve citation validation, and define stage deadlines plus safe fallbacks before failures occur.

What will you be able to do?

• Design cache keys that preserve authorization and versions.
• Decide which retrieval and generation artifacts are cacheable.
• Stream without pretending unvalidated text is final.
• Allocate deadlines across stages.
• Define degraded and abstention modes.

What belongs in a cache key?

Depending on the layer: tenant/policy version, principal scope or authorization fingerprint, normalized query, query model and revision, retrieval configuration, source/index generation, reranker, prompt version, generation model, locale, and safety policy. If any omitted field can change the answer or access, the cache may be wrong.

Embedding caches should store only approved input hashes or content under retention policy. Shared semantic caches are particularly risky across tenants.

What can be streamed?

Streaming improves perceived latency, not total work. A model may emit a claim before its citation object arrives or later produce invalid structure. Options include buffering until validation, streaming clearly provisional prose, or streaming only validated sections. Never execute tools from partial output.

What are safe fallbacks?

• reranker failure → serve reviewed first-stage results;
• semantic provider failure → lexical-only search with a visible limitation;
• generator failure → return ranked sources rather than fabricate an answer;
• retrieval failure → fail closed for protected content;
• stale cache uncertainty → miss the cache.

Define which failures may use stale data and for how long.

How do you verify it?

Test access revocation, source update, model migration, policy change, tenant collision, cache stampede, provider timeout, mid-stream disconnect, invalid final citations, and circuit recovery. Confirm deletion invalidates every cache path within its SLO.

What breaks in production?

• Cache keys include query text but not tenant policy.
• Stale answers survive a source correction.
• Partial streamed output bypasses validators.
• All stages consume the full request timeout independently.
• Fallbacks silently reduce security or evidence.

AI pair-work prompt

Build a caching and degradation matrix for this AI retrieval path [paste]. For each stage, define key, value, TTL, invalidation, sensitivity, stampede control, deadline, retry, circuit breaker, fallback, user disclosure, and metric.

Verification contract: Run revocation and version-change tests against every cache and prove the final deadline is the sum of bounded stage budgets, not nested full timeouts.

Check your understanding

1. Why can semantic caches cross security boundaries?
2. What is a safe generator fallback?
3. Name four version fields in a RAG response cache key.

Official references

• PostgreSQL LISTEN
• PostgreSQL NOTIFY
• PostgreSQL statement timeout