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Post-processing

Formats, cites, and sanity-checks output.

What It Does

Takes the raw output from the LLM and applies various transformations such as formatting (bullet points, markdown, JSON), fact-checking against source documents, adding citations, filtering inappropriate content, and summarizing when needed. It serves as a quality control layer before delivering the final response.

Why It Matters

Even with context, LLMs can produce outputs that benefit from an extra check. Post-processing ensures the final answer is not only correct but also presented appropriately. It increases trust and usability by delivering polished, verified responses aligned with application-specific requirements.

Common Challenges

  • Implementing reliable automated fact-checking against source documents
  • Modifying outputs without changing their meaning or introducing errors
  • Adding accurate citations that correctly link statements to sources
  • Balancing thoroughness with response latency
  • Creating robust systems that can handle unexpected model outputs
  • Maintaining output quality while scaling to high query volumes

Interactive Demo

Formatting Options

Raw LLM Output

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Processed Output

Select options and click "Process" to see the result

Select options from the tabs above and click "Process" to transform the raw LLM output according to your specifications.

Skip the Complexity

Building a robust Post-processing solution is challenging. Respeak's Enterprise RAG Platform handles this complexity for you.

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