Course Overview
Scaling technical publishing without degrading quality requires moving past basic prompt engineering into deterministic API workflow orchestration. This course guides search architects and software engineers through constructing autonomous content generation pipelines that write, cross-verify, structure, and publish multi-modal web assets directly into your CMS.
You will build custom Python wrappers combining LLM APIs (GPT-4o, Claude 3.5 Sonnet) with headless browsers, automated vector similarity checkers, and schema generators to guarantee high editorial integrity at 10x throughput.
Curriculum Breakdown
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Module 1: Programmatic Prompt Chaining & Multi-LLM Orchestration
Build modular LangChain and LlamaIndex pipelines that route specific sub-tasks to specialized micro-models for optimal token cost and speed.
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Module 2: Automated Fact-Checking & Hallucination Guardrails
Implement real-time ground-truth verification using Google Search APIs and Wikidata triples to catch numerical or factual hallucinations before publishing.
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Module 3: Multi-Modal Asset Generation & Media Injection
Programmatically generate custom SVG diagrams, Midjourney images via API, and inline data tables aligned with target article entities.
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Module 4: Headless CMS Integration & Continuous Publishing
Connect your Python generation scripts to WordPress REST API, Webflow Webhooks, or Sanity.io studio workflows for zero-touch deployment.
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Module 5: Human-In-The-Loop (HITL) QA Interfaces
Design low-code Streamlit dashboard interfaces for editorial teams to rapidly review, approve, or refine AI-generated technical drafts.
Technical Frameworks & Tooling
All scripts and workflows taught in this masterclass utilize industry-standard open-source libraries and APIs:
• Orchestration Engine: LangChain Framework Docs
• Data Indexing: LlamaIndex Documentation