A step-by-step guide to building a production-grade research automation pipeline using n8n, Groq (Llama 3.3 70B), and academic APIs (Semantic Scholar, OpenAlex, arXiv, PubMed). The pipeline covers six stages: centralized configuration, parallel API collection with failure isolation, data normalization and DOI-based deduplication, structured LLM extraction with strict JSON prompting, relevance and quality scoring, and delivery to Google Sheets. The tutorial also covers lightweight eval checks to catch silent regressions in AI extraction, and practical error handling patterns like batching, retries, and partial-failure recovery.
Table of contents
PrerequisitesThe Problem: Research Takes Too LongThe Tech StackThe Project Structure: How to Think About an n8n Workflow Like SoftwareStage 1: Centralised ConfigurationStage 2: Parallel API Collection (With Failure Isolation)Stage 3: Normalisation and Deduplication (DOI-first, Title fallback)Stage 4: AI-Powered Content Extraction (Strict JSON)Stage 5: Scoring and SynthesisBeginner-Friendly Evals: Retrieval and Extraction QAKey Learnings and Error HandlingConclusionSort: