If I Needed Resilient Software in 30 Days, Here’s the SOLID Way I’d Do It
SOLID for modern software: single responsibility, open/closed, Liskov, interface segregation, dependency inversion—clean interfaces, swappable parts, safer fallbacks, testable pipelines.
By Rev.AISomething
Features break for boring reasons: brittle dependencies, hard-coded providers, and untested fallbacks. SOLID—Single Responsibility, Open/Closed, Liskov substitution, Interface Segregation, Dependency Inversion—still applies when you add AI or any external service. Here’s a concise playbook to keep changes cheap and behavior predictable.
What you’ll get:
- SOLID applied to service/AI pipelines without code
- Swap-safe adapters (Open/Closed, Liskov)
- Safer fallbacks and caching boundaries (Single Responsibility)
- Thin, testable interfaces for clients (Interface Segregation)
- Dependency Injection for providers and evaluators (Dependency Inversion)
- A short checklist to keep regressions low
S — Single Responsibility
Keep ingestion, retrieval, generation, and post-processing as separate concerns. One module fetches contexts, another calls a service, another formats the answer. This makes caching, testing, and swapping easier.
Why it helps:
- When a provider is slow, you can cache or swap just the retrieval stage without touching answer formatting.
- Tests stay small: feed canned inputs to one stage and assert outputs, no network required.
- Incidents shrink in scope because each stage has a single metric to watch (e.g., retrieval p95, generation error rate).
O — Open/Closed
Add new providers or tools by adding implementations, not rewriting callers. Define a stable contract for “complete,” “embed,” or “rerank,” and register new implementations behind it. Guardrails (limits, defaults) stay in the adapter, not scattered across the codebase.
Why it helps:
- Adding a new model or tool is additive; existing paths keep working.
- Risk is contained to the new adapter; blast radius is smaller during rollouts.
- You can feature-flag new implementations without changing business logic.
L — Liskov Substitution
Any provider should be swappable without breaking the rest of the pipeline. Keep return shapes consistent: success plus metrics; failure with clear error codes. Hide provider-specific fields from callers; keep them in logs for debugging.
Why it helps:
- Dashboards and billing rely on stable fields (latency, tokens, cache hit); you keep them intact across swaps.
- Rollbacks are low-friction because interfaces match.
- Fallback chains work because every step can substitute for another without shape mismatches.
I — Interface Segregation
Expose small, purpose-built interfaces. UI code should see “ask(question) → answer + sources,” not embeddings or headers. Batch jobs can use a thin client without UI concerns. Swapping transport (HTTP → gRPC) or provider should not leak into callers.
Why it helps:
- Fewer breaking changes when you change transport or provider.
- Teams can test their slice in isolation (UI doesn’t need embedding details; batch jobs don’t need UI concerns).
- Security is cleaner: fewer surfaces expose internal or provider-specific data.
D — Dependency Inversion
Depend on interfaces, inject concrete implementations at the edges (env/config). Tests inject fakes; load tests can inject “budget guardrail” clients; production swaps providers via config, not code changes.
Why it helps:
- Environment-based switches (staging vs prod) don’t require redeploys.
- Load and chaos tests can simulate failures by swapping implementations.
- New vendors or versions can be trialed safely behind configuration.
Resilience Patterns for Services (Including AI)
- Cache at the right layer: cache retrieval results (contexts) separately from model outputs. Use short TTLs for answers; slightly longer for retrieval. Deduplicate requests by prompt hash.
- Fallbacks: define ordered fallbacks (primary model → cheaper model → template response) with clear metrics on when fallbacks trigger.
- Budgets: enforce
max_tokensand reject overlong inputs early. Add per-tenant rate limits and per-request ceilings. - Idempotency: hash inputs to dedupe retries and avoid double-billing.
- Timeouts: set timeouts per stage; fail fast on retrieval so model calls aren’t wasted when no context exists.
- Structured logs: log
stage,latency_ms,tokens_in,tokens_out,provider,hit_cache,fallback_used.
Testing and Evals
- Unit-test adapters with fixtures for success, rate limits, and invalid requests.
- Add contract tests for each provider to ensure the interface stays compatible.
- Build a small eval set (50–100 Q&A pairs) and run it on each model; store scores so regressions are obvious when swapping.
- Smoke-test fallbacks by forcing the primary client to fail and verifying the chain.
Operational Runbook
- Dashboards: p50/p95 latency per stage; error rate by provider; cache hit rate; fallback activation count; spend per tenant.
- Alarms: elevated timeouts, rising
fallback_used, or cache hit rate dropping below a target. - Rollouts: feature-flag new models; dark-launch them and compare outputs before switching traffic.
- Incidents: if providers spike latency, shorten TTLs and raise cache durations to shield users; if accuracy drops, revert to last known good model and reranker.
Checklist to Ship
- Responsibilities split by layer (inputs, domain logic, outputs) with tests per layer.
- External services sit behind clear interfaces; adapters are swappable via config.
- Input validation, timeouts, and rate limits enforced at each boundary.
- Caching rules defined per layer; metrics emit hit/miss/age so drift is visible.
- Fallback paths and rollback plan defined and exercised.
- Contract tests for adapters plus a small acceptance/eval suite tracked when swapping providers.