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A RAG answer can cite the right document and still add an unsupported instruction.
For Shamrock Marketing, I built technical knowledge retrieval with Azure AI Search and Azure OpenAI, using product and workflow metadata plus document and page citations.
Here's a test I'd include for this kind of system: a source describes a procedure but gives no inspection interval. The answer explains the procedure correctly, then adds 'repeat every 30 days' beside the same citation.
I'd evaluate retrieval and answer support separately. Did we retrieve the relevant passage? Does that passage support each instruction, number and condition in the response?
A working citation link gives reviewers a way to check. The answer still needs to stay within what the source says.
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Shamrock Marketing: Technical knowledge RAG | Kapil Chauhan
A citation-backed RAG system for standards, workflows, and product knowledge
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CRAG — Self-Correcting Research Assistant
Overview
A research-assistant project exploring how AI applications can retrieve information and improve the quality of their responses through a self-correction workflow.
Project focus
Python-based AI application development.
Retrieval and research workflow design.
Improving answer reliability through evaluation and correction.
Technologies: Python, AI, retrieval-augmented generation.
Self correction is where RAG gets useful. How do you judge a retrieved chunk as bad enough to trigger a new search?
Most agencies think AI SEO is just about adding Schema markup.
They are missing the main part: How AI actually connects brands to topics.
Here is how ChatGPT and Perplexity decide which business to recommend:
Brand Topic Connection: AI builds a mental map. If your brand isn't constantly mentioned next to your core service online, AI won't trust you.
Entity Sources: AI checks structured sources like Wikidata, Crunchbase, and niche directories to verify if your business is real and authoritative.
Co-mentions: If your brand is mentioned alongside top industry leaders across blogs and forums, AI treats you as a top choice.
Traditional SEO helps you rank on Google.
Entity optimization helps you get recommended by AI.
Want my AI Brand Visibility Checklist to see if AI trusts your brand?
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Agree on the topic connection part. We saw the same with B2B lists: a brand gets named when third party pages describe it consistently, not when the markup is perfect.
Not every repetitive business task deserves to be automated.
Before building an AI workflow, I'd ask five questions:
Frequency: How often does this task happen?
Time: How much manual effort does it consume?
Consistency: Does the process follow repeatable steps?
Risk: What happens if the automation gets it wrong?
Value: What could the team do with the time saved?
A task that takes five minutes once a month probably doesn't need an elaborate automation.
But a task that takes 20 minutes every day, follows predictable steps, and regularly gets delayed might be a good candidate.
Even then, automation isn't always the answer. Sometimes a clearer process or a simple rule is enough.
My approach is to understand the bottleneck first, then decide whether AI, automation, a simpler workflow—or no change at all—is the right solution.
What's one repetitive task in your business that you'd love to eliminate?
#WorkflowAutomation #AIAutomation #BusinessOperations
I find the point about simple rules often being enough is critical. Many teams skip that step and jump straight to complex n8n workflows, only to realize a basic if then logic or a shared checklist solved the issue without the maintenance overhead.