![]() Latency is, on the other hand, mostly the result of the need to make multiple LLM roundtrips to consume external data sources to extend the LLM initial response. ![]() While this solution is, in principle, relatively easy to implement manually, supporting the full Markdown specification requires using an off-the-shelf parser, says Goral. ![]() The stream processor either passes through the characters as they come in, or it updates the buffer as it encounters Markdown-like character sequences. To solve this problem, Spotify uses a buffering parser that does not emit any character after a Markdown special character and waits until either the full Markdown expression is complete, or an unexpected character is received.ĭoing this while streaming requires the use of a stateful stream processor that can consume characters one-by-one. This implies that Markdown expressions cannot be correctly rendered until they are complete, which means that for a short period of time Markdown rendering is not correct. The same problem applies to links and all other Mardown operators. Streaming a Markdown response returned by the LLM leads to rendering jank due to the fact that special Markdown characters, like *, remain ambiguous until the full expression is received, e.g., until the closing * is received. ![]() While using a Large Language Model chatbot opens the door to innovative solutions, Spotify engineer Ates Goral argues that crafting the user experience so it is as natural as possible requires some specific efforts in order to prevent rendering jank and to reduce latency. ![]()
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