AI integrationprototypinglegacy

Playbook Diagrams for Rapidly Prototyping LLM-Powered Features in Existing Apps

ddiagrams

2026-02-18

10 min read

Diagrams and templates to add LLM features to legacy apps quickly using wrappers, API adapters, and staged rollouts.

Hook: Rapidly add on-device and edge-capable LLMs to legacy apps without a full rewrite

If you’re an engineering lead or platform owner, your pain is familiar: product teams demand AI features — summarization, question answering, smart search — but your app is monolithic, brittle, and full of technical debt. You need to prototype LLM features quickly, prove value, and roll them into production without a big migration. This playbook supplies diagrams, templates, and a concrete rollout path to do exactly that — using wrappers, API adapters, and staged rollouts so changes to the core app are minimal.

Executive summary — what you’ll get

This article is a practical playbook for prototyping and shipping LLM features into legacy apps in 2026. You’ll find:

High-value architecture diagrams (component, sequence, data-flow) you can copy and adapt.
Code-level adapter and wrapper patterns (Node/Python templates) that sit outside your core codebase.
Staged rollout and telemetry patterns (shadow, canary, feature-flagged, rollback-safe).
Collaboration and versioning workflows for diagrams and models, plus export and embedding best practices.
Actionable checklist to get from prototype to safe production in weeks.

The 2026 context: Why this matters now

Late 2025 and early 2026 accelerated two trends that change the risk/reward balance for adding LLM features to legacy systems:

Model access and orchestration matured: more specialized model endpoints, cheaper embedding pipelines, and multi-model orchestration are widely available from major providers.
AI-enabled developer hardware and on-device runtimes (a surge of new inference hardware in 2025) let teams prototype privacy-friendly, low-latency features for niche apps.

These trends make it possible to prototype external LLM integrations quickly and with predictable costs — if you use the right integration patterns. The following diagrams and templates are optimized for that reality.

Core integration patterns (one-line summaries)

Wrapper pattern: Add a thin service that wraps LLM calls and encapsulates prompts, retry logic, caching, and logging.
API adapter: Map your legacy app’s payloads to modern LLM request/response shapes (and back) with an adapter layer.
Staged rollout: Use shadow testing + feature flags + canary to validate relevance and cost before full release.

Playbook Diagram 1 — Component diagram (drop-in wrapper + adapter)

Use this as the baseline: attach an external LLM service next to your app rather than inside it. The wrapper acts as the single integration point for prompts, embeddings, and model selection.

  +-----------------+        +-----------------+        +---------------------+
  | Legacy App      |  --->  | Integration     |  --->  | LLM Provider(s)     |
  | (UI, DB, APIs)  |        | Wrapper /       |        | (OpenAI, Anthropic, | 
  +-----------------+        | API Adapter     |        | on-prem model, etc)  |
            |                 +-----------------+                |
            |                           |                        +--+
            |                           V                           |
            |                  +-----------------+                 |
            +----------------> | Vector DB / Cache | <---------------+
                               | DB / Rate-limit |                 
                               +-----------------+

Key decisions: keep the wrapper inside your platform boundary but outside your legacy codebase. That makes rollbacks painless and allows independent scaling.

Component diagram notes

Wrapper responsibilities: consolidate prompt templates, apply instruction tuning, do response validation/sanitization, enforce rate-limits, and manage retries.
API Adapter: handle content normalization (HTML -> plain text), map legacy IDs, and enforce RBAC for user-level LLM calls.
Vector DB / Cache: host embeddings, chunked documents, and store short-term responses to reduce costs and improve latency.

Playbook Diagram 2 — Sequence diagram for a typical feature (e.g., smart search)

Sequence diagrams clarify where latency and failures happen. Use this to instrument traces and SLOs.

  User -> Legacy App: search(query)
  Legacy App -> Integration Wrapper: normalize(query)
  Wrapper -> Vector DB: semantic_search(query)
  Wrapper -> LLM Provider: optional_rerank(prompt_with_context)
  LLM -> Wrapper: answer/reranking
  Wrapper -> Legacy App: results
  Legacy App -> User: display(results)

Place observability hooks at each arrow: request IDs, timings, model name & cost, and content hashes. That supports cost governance and audits — essential in staged rollouts.

Template: Lightweight API Adapter (Node.js example)

This template shows the minimal adapter that maps your app request to an LLM payload, calls the wrapper service, and maps the result back. Keep this file as the only change in the legacy app when possible.

  // pseudocode - adapter.js
  const fetch = require('node-fetch')

  async function callLLMFeature(userId, appPayload) {
    const normalized = normalizePayload(appPayload)
    const response = await fetch(process.env.WRAPPER_URL + '/v1/llm', {
      method: 'POST',
      headers: { 'Authorization': `Bearer ${process.env.WRAPPER_KEY}` },
      body: JSON.stringify({ userId, normalized })
    })
    const json = await response.json()
    return denormalizeForApp(json)
  }

  module.exports = { callLLMFeature }

Template: Wrapper service responsibilities (checklist)

Prompt templates and prompt versioning
Model selection and fallback logic
Embeddings orchestration and chunking
Caching & vector DB access
Request logging, cost attribution, and telemetry
Response sanitation and safety filters
Rate limiting, concurrency control, retry policies

Staged rollout playbook — practical sequence

Shadow mode: let the wrapper receive production traffic in parallel; never return LLM results to users. Compare outputs for precision, latency, cost.
Human-in-the-loop A/B: return LLM responses to a small cohort of internal users; collect qualitative feedback and edge-case failures.
Canary release: enable the feature for a percentage of real users behind feature flags. Monitor errors, P99 latency, and token costs per request.
Cost cap & autoscale: enforce hard cost caps on the wrapper; have automated rollback triggers for unexpected token spikes.
Full rollout + continuous improvement: iterate on prompts, model choice, and chunking strategy; version prompts and models explicitly.

Telemetry and KPIs to track during rollout

Uptime and P95/P99 latency of wrapper and provider calls
Tokens consumed per feature and projected monthly spend
Feature acceptance rate (clicks, saves, session length)
Semantic drift or degradation (QA sampling)
False positives/negatives for hallucination-sensitive features

Versioning: prompts, adapters, and models

Treat prompts, adapter schema, and model selection as first-class versioned artifacts. In 2026, teams increasingly use CI pipelines to test and gate prompt updates alongside code.

Prompt registry: store prompts in a git-backed registry with metadata (owner, risk level, change log).
Adapter schema: use JSON schema for adapter request/response types and run schema validation in CI.
Model pinning: pin the model and model weights (or API endpoint) per environment — dev/stage/prod — to avoid surprise regressions when providers update models.

Example: tag prompts like prompt_v1.3@2026-01-12 and include an automated QA job that samples 100 responses and checks for forbidden outputs before merging.

Embedding & Retrieval: pragmatic RAG (retrieval-augmented generation) diagram

For many legacy apps the highest ROI is RAG (retrieval-augmented generation). Here’s a minimal RAG pipeline that your wrapper can orchestrate.

  +-------------+    +------------+    +-------------+   +------------+
  | Document    | -> | Chunking   | -> | Vector DB   | ->| Wrapper/   |
  | store (DB)  |    | + embed    |    | (milvus etc)|   | LLM model  |
  +-------------+    +------------+    +-------------+   +------------+
                                  ^
                                  |
                              Re-rank /
                              Rerank cache

Practical tips: chunk by semantic boundaries (sections, headings), keep overlap small (10–30%), and TTL-evict embeddings for frequently changing documents.

Collaboration & workflow integration (Diagrams + Exports)

Your diagrams and templates must be part of the team’s flow. Follow these practices to keep architecture living and auditable.

Diagrams-as-code: store component and sequence diagrams in Mermaid or PlantUML alongside adapters and prompts in the same repository. This enables CI to validate diagram syntax and link diagrams to code changes.
Export formats: generate SVG for docs, PNG for tickets, and an exportable JSON or draw.io file for handoffs. Keep one canonical SVG per feature under /docs/diagrams/.
Embed in docs: render diagrams in Confluence, Notion, or your internal docs site. Add a short “how-to-run-locally” snippet and a link to the wrapper’s health endpoint.
Ownership: attach an owner and SLO to each diagram and wrapper component. Use PR templates that require the owner’s sign-off for model or prompt changes.

Sample Mermaid component snippet (copyable)

  %% Mermaid component diagram (paste into Mermaid-enabled docs)
  graph LR
    LegacyApp[Legacy App]
    Wrapper[LLM Wrapper & Adapter]
    VectorDB[(Vector DB)]
    LLM[LLM Provider]
    LegacyApp -->|API call| Wrapper
    Wrapper --> VectorDB
    Wrapper --> LLM

Case study: Adding a summarization feature to a legacy CRM (6-week plan)

Context: a decade-old CRM with a monolith backend and a PostgreSQL store. Product wants “auto-summary” on contact pages. Goal: prototype, validate product-market fit, and roll out to 10% of users in 6 weeks.

Week 0-1: Build wrapper service (Node + Express). Implement prompt template v0, basic logging, and a single endpoint /v1/summarize.
Week 1-2: Create adapter in the monolith (2 files) that calls /v1/summarize and returns summary text. Ship to internal users behind a feature flag.
Week 2-3: Run shadow mode against current search/summaries for 3,000 sample contacts. Collect latency & hallucination rates.
Week 3-4: Implement vector DB for long notes; test RAG for documents >2k tokens. Add human-in-the-loop QA for low-confidence outputs.
Week 4-5: Canary to 10% users, monitor cost per user and acceptance metrics. Add rollback triggers when token cost > budget or hallucination > threshold.
Week 5-6: Iterate prompts per feedback, add caching for 24h per contact, and expand rollout.

Outcome: prototype to production in 6 weeks with only two small changes to the legacy codebase and a single wrapper service to maintain.

Risks and mitigations

Hallucinations: mitigate with RAG, tooling for provenance, and human review for high-risk outputs.
Cost spikes: enforce token caps, per-feature quotas, and token-cost telemetry with automatic pause/rollback.
Data leakage: obfuscate PII in prompts, use on-prem or private endpoints for sensitive data, and keep an audit trail for every prompt and response. Also follow a data sovereignty checklist for multinational data flows.
Stability: pin models per environment and test prompt/model combos in CI before promotion.

Advanced strategies and 2026 predictions

Looking forward from 2026, expect these to be mainstream in production LLM integrations:

Fine-grained model orchestration: middleware that routes sub-tasks to specialist LLMs (NLP, code, summarization) based on runtime signals.
Automated prompt-linting: static analysis for prompts that checks for PII, hallucination risk, and test coverage before deployment.
Hybrid on-device + cloud inference: low-latency sensitive features will use local models (edge/AI HAT devices) as cache + cloud for heavy lifting.
Standardized metadata: models, prompts, and adapters will carry lineage metadata (who changed what, when, and why) integrated with governance flows and SOC2 controls.

These shifts make the wrapper + adapter pattern even more powerful: it isolates change and centralizes governance.

Actionable checklist to get started this week

Pick one high-value LLM feature (search, summarization, code assist) and measure current manual effort to perform it.
Spin up a small wrapper service: implement prompt templating, logging, and a single health endpoint.
Add a minimal adapter in the legacy app (one file) and wire it behind a feature flag.
Run shadow mode for 1 week and collect telemetry: latency, token cost, and QA samples.
Create a prompt registry entry and pin the model. Add a CI job that runs response sanity checks on pulls that touch prompts.

"Start external, keep changes minimal, and prioritize observability(" — recommended rule for legacy LLM integrations in 2026)

Where to keep diagrams, code, and templates

Repository layout: /infrastructure/wrappers/, /prompts/, /diagrams/ (Mermaid/PlantUML), /docs/
Use git tags for prompt versions and tag PRs with prompt IDs for traceability.
Include exported SVGs in release notes and link to the wrapper’s telemetry dashboards.

Final takeaways

In 2026, the fastest path to adding reliable LLM features to legacy apps is the wrap, adapt, and stage approach: add an external wrapper, map payloads through adapters, and roll features out gradually with strong telemetry and versioning. This strategy minimizes changes to fragile code, delivers value quickly, and centralizes governance and costs.

Call to action

Ready to prototype an LLM feature in your app this week? Start with the wrapper template above and run a 7-day shadow test. If you want a ready-to-import diagram package and adapter templates tailored for Node, Python, and Java, download our Playbook Diagrams bundle and step-by-step checklist at diagrams.us/playbooks/llm-legacy. Get the bundle, run the shadow mode, and loop in your platform owner for the first canary.

diagrams

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.