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@legitalgorithmswithpeter: Test automation with Playwright — what does it actually do? 🎭🧪 Imagine manually testing your login, search, cart, and checkout flow after every code change. 😭 That’s where Playwright comes in. 🚀 It’s a browser automation and end-to-end testing framework that can control Chromium, Firefox, and WebKit. Your test can: 🌐 Open the website 📧 Enter login credentials 🖱️ Click buttons 🛒 Add products to a cart ✅ Verify that the expected result actually appears And Playwright does more than just click buttons. ⏳ Automatic waiting 🔍 Reliable locators 🌐 Network request mocking 🧑‍💻 Isolated browser contexts ⚡ Parallel test execution 📸 Screenshots, videos, and traces for debugging You can even add it to your CI/CD pipeline so every important code change automatically tests critical user journeys before deployment. 🤖 But don’t use Playwright for everything. ⚙️ Unit tests → Individual functions 🔗 Integration tests → Components working together 🎭 Playwright → Critical end-to-end user workflows The goal isn’t just automation. It’s automatically proving that your application still works. 🔥 #PlaywrightTesting #EndToEndTesting #BrowserAutomation #TestAutomation #QualityEngineering

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Saturday 29 August 2026 22:46:44 GMT
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When an interviewer says “we have millions of customer support tickets…” they’re not testing AI. they’re testing systems thinking. Here’s what that actually looks like 👇 I’d break it into 5 parts: ingestion, embeddings, retrieval, generation, monitoring 1️⃣ Ingestion is offline, not request-time At this scale, everything is async • Stream tickets from DB / event logs • Normalize messy text (typos, threads, duplicates) • Thread conversations properly (not just single messages) • Chunk by conversation flow, not fixed tokens • Attach metadata (user, product, issue type, timestamp) This NEVER touches your request path 2️⃣ Embeddings + indexing built for scale You don’t embed on demand • Batch jobs for new tickets (queue-based) • Distributed vector DB (Qdrant, Milvus, etc.) • Hybrid search (BM25 + dense vectors) • Sharding by product / issue type • Store metadata alongside vectors Key insight: metadata filtering > vector search 3️⃣ Retrieval + generation (tight path) Keep this path fast and lean: query → metadata filter → cache → hybrid search → rerank → LLM • Most queries hit cache • Many never reach vector search • Rerank small candidate sets only • Send 5–10 HIGH quality chunks More data ≠ better answers Relevance wins every time 4️⃣ Caching is everything This is where scale actually happens • FAQ cache (repeat questions) • Retrieval cache (top ticket clusters) • Embedding cache (hot queries) Real flow: query → cache → (miss) retrieve + LLM → store result This is how you cut cost AND latency 5️⃣ Monitoring closes the loop Without this, your system slowly dies • Are we retrieving the right tickets? (recall@k) • Are answers actually helpful? (feedback) • Latency + cache hit rate • Auto re-embed as new tickets come in BOTTOM LINE: RAG at this scale is NOT an AI problem it’s a search + data + caching problem Most engineers build chatbots Real AI engineers build infrastructure #aiengineer #ai #embedding #rag #softwareengineer
When an interviewer says “we have millions of customer support tickets…” they’re not testing AI. they’re testing systems thinking. Here’s what that actually looks like 👇 I’d break it into 5 parts: ingestion, embeddings, retrieval, generation, monitoring 1️⃣ Ingestion is offline, not request-time At this scale, everything is async • Stream tickets from DB / event logs • Normalize messy text (typos, threads, duplicates) • Thread conversations properly (not just single messages) • Chunk by conversation flow, not fixed tokens • Attach metadata (user, product, issue type, timestamp) This NEVER touches your request path 2️⃣ Embeddings + indexing built for scale You don’t embed on demand • Batch jobs for new tickets (queue-based) • Distributed vector DB (Qdrant, Milvus, etc.) • Hybrid search (BM25 + dense vectors) • Sharding by product / issue type • Store metadata alongside vectors Key insight: metadata filtering > vector search 3️⃣ Retrieval + generation (tight path) Keep this path fast and lean: query → metadata filter → cache → hybrid search → rerank → LLM • Most queries hit cache • Many never reach vector search • Rerank small candidate sets only • Send 5–10 HIGH quality chunks More data ≠ better answers Relevance wins every time 4️⃣ Caching is everything This is where scale actually happens • FAQ cache (repeat questions) • Retrieval cache (top ticket clusters) • Embedding cache (hot queries) Real flow: query → cache → (miss) retrieve + LLM → store result This is how you cut cost AND latency 5️⃣ Monitoring closes the loop Without this, your system slowly dies • Are we retrieving the right tickets? (recall@k) • Are answers actually helpful? (feedback) • Latency + cache hit rate • Auto re-embed as new tickets come in BOTTOM LINE: RAG at this scale is NOT an AI problem it’s a search + data + caching problem Most engineers build chatbots Real AI engineers build infrastructure #aiengineer #ai #embedding #rag #softwareengineer
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