Pavan @pavancodes - Twitter Profile

Pavan @pavancodes

2 months ago

@BunnyXTarak Add me

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Suraj Sharma

@suraj_sharma14

3 months ago

CANCEL your weekend plans. You NEED to: • Build a RAG system that cites sources with page numbers • Implement hybrid search (dense + sparse) for better retrieval • Add reranking with cross-encoders for top-10 accuracy • Set up chunking strategies (500 tokens, 50 overlap minimum) • Build query expansion for better recall • Add metadata filtering for scoped retrieval • Implement citation grounding to prevent hallucinations • Create an eval harness with 50+ golden test cases • Track retrieval metrics: hit rate, MRR, NDCG • Add fallback to web search when confidence is low • Build query rewriting for ambiguous questions • Implement parent document retrieval for context • Add embedding caching to reduce latency 80% • Use colbert or late interaction for better accuracy • Build a RAG dashboard showing retrieval quality • Test with adversarial queries that should return nothing • Document your chunking strategy and why it works • Benchmark against naive RAG and show improvement You have way too much to do. Bookmark & Repost.

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Roan

@RohOnChain

3 months ago

My friend makes $850,000/year as an Anthropic AI engineer. No MIT. No Stanford. No PhD. I asked him how he broke into AI from scratch. He sent me a course that was never supposed to get out. A developer teaching to build self-improving AI agents from scratch. You will learn exactly how AI agents like Claude Code and OpenClaw are actually built. You won't find anything better about becoming an AI engineer in 2026 than this video. I watched it last night. Halfway through, I realized I could land a role at a top AI lab in months, not years. Bookmark this and read the article below.

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Suraj Sharma

@suraj_sharma14

5 months ago

If I had to start from zero today. And land an Applied AI role in 6 months. I'd do this. Stage 1 : Python + Production APIs FastAPI, async, error handling, webhooks, REST/GraphQL, third-party SDK integration. Stage 2 : LLM Fundamentals for Builders Context windows, model routing, token economics, embeddings, cost/latency tradeoffs. Stage 3 : Structured Outputs + Prompt Ops Pydantic validation, function calling, prompt versioning, template management, unit evals. Stage 4 : RAG + Knowledge Grounding Chunking strategies, hybrid search, rerankers, vector DBs, metadata filtering, citation tracking. Stage 5 : AI Workflows + Orchestration Tool calling, state machines, human-in-the-loop, retry logic, fallback chains, session memory. Stage 6 : Production-Ready AI Applications Domain-specific copilots, automation pipelines, streaming UIs, graceful degradation, rate limiting. Stage 7 : Evaluation + Reliability Engineering Accuracy scoring, hallucination detection, RAGAS/DeepEval, regression testing, A/B output validation. Stage 8 : Inference + Infrastructure Optimization vLLM, Ollama, quantization, KV caching, speculative decoding, token cost tracking, edge deployment. Stage 9 : Deployment + Observability Docker, CI/CD, cloud hosting, distributed tracing, structured logging, alerting, canary releases. Stage 10 : AI Security + Guardrails Input/output filtering, prompt injection defense, PII redaction, compliance checks, sandboxed execution. Stage 11 : Open Source + Portfolio Ship end-to-end apps publicly, write architecture docs, record demo walkthroughs, publish eval reports. Stage 12 : Apply Applied AI Engineer, GenAI Developer, AI Integration Engineer, LLM Solutions Architect, AI Product Engineer. Most people stay stuck watching tutorials. Builders get hired.

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