Preventing GPT hallucination in automated content pipelines: how I structure Make.com flows with data injection
The article discusses how to prevent GPT hallucinations in automated content pipelines by restructuring data flows. The author shares their experience with a Make.com pipeline for generating sports betting articles that initially produced incorrect information. By implementing a data injection approach, they ensured that GPT only received verified facts, thus eliminating hallucinations.
- ▪The author initially faced issues with GPT generating incorrect sports betting articles due to missing data.
- ▪A structural change was made to the data flow, adding validation and constraint modules to ensure GPT only received verified facts.
- ▪The new system prevents hallucinations by marking missing data and adjusting prompts accordingly.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3944466) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Kevin Seeberger Posted on May 25 • Originally published at seeberger-solutions.com Preventing GPT hallucination in automated content pipelines: how I structure Make.com flows with data injection #ai #architecture #automation #llm I run a Make.com pipeline that produces daily sports betting articles. Odds API in, API-Football in, aggregation in the middle, GPT-4o for the writing, Google Docs out. Looks great on the diagram. Worked beautifully in testing. Then it shipped.
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