Results presentation
At this point, we have covered techniques for selecting appropriate GenAI models, crafting effective prompts, and guiding the models to produce high-quality results. Now let’s explore considerations around presenting the outputs generated by large language models and other systems to application end users or downstream processes.
How LLM-produced content gets rendered and exposed is heavily dependent on the specific use case and application architecture. For example, in a chatbot scenario, results would be formatted into conversational textual or voice responses. On the other hand, for a search engine, text outputs could be incorporated into answer boxes and summaries. Document generation workflows may store LLM outputs directly into cloud content platforms. The possibilities span many formats.
Some technical aspects are common across different result presentation approaches. Text outputs often require post-processing such as Markdown tagging removal...
