If I Needed Resilient Software in 30 Days, Here’s the SOLID Way I’d Do It

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

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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).

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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.

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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.

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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.

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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.

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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_tokens and 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.

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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.

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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.

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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.