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podium-rag-context-bridge

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Bridge a live Podium call transcript or webchat turn to an LLM by fetching relevant historical conversation context as a structured RAG bundle — vector search over embedded prior conversations + reranking + live Podium contact lookup, merged under a hard latency budget that keeps the call answerable in real time. Use when wiring a transcription-driven agent loop to an LLM that needs cross-channel customer memory, building the substrate that turns "transcript chunk" into "LLM-ready prompt with context", or hardening an existing RAG pipeline against staleness, reranking noise, PII leakage, token-budget overflow, and missed-the-call latency. Trigger with "podium rag", "podium llm context", "podium transcript to llm", "podium retrieval", "podium vector search", "podium real-time context", "podium agent grounding".

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Nov 12, 2026
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# Podium RAG Context Bridge

## Overview

Take a live transcript chunk (or webchat turn) and emit a structured RAG context bundle the calling LLM can drop into its prompt. This is not a chatbot. This is the substrate that sits between Podium's transcription stream and whatever LLM your agent loop is using — fetching the right historical context, fast enough to matter, in a shape the model can actually consume.

The substrate combines two retrieval surfaces: a **vector store** of past Podium conversations (embedded at ingest by `podium-conversation-history-export`) and a **live Podium API lookup** for the contact's fresh state (phone, opt-out, location, last-seen). The two surfaces answer different questions — vectors answer "what did this person ever say about this topic," the live API answers "is the phone number we're about to dial actually still their phone number." A naive RAG pipeline collapses them and ships drift to the model.

The six production failures this skill prevents:

1. **Relevance scoring picks wrong historical context** — naive cosine similarity over top-K=5 chunks surfaces the five most similar embeddings; on real Podium corpora most of those are off-topic boilerplate ("thanks for reaching out", "have a great day"). The model sees noise, generates a generic reply, and the operator loses the customer. Fix: cross-encoder reranking + per-contact filtering BEFORE the model sees anything.
2. **Vector store stale vs live Podium data drift** — the contact updated their phone yesterday; today the vector store still embeds the old number. The model answers "I'll call you back at (555) 0100" using a number that hasn't worked in 16 hours. Vector recall and live state are different SLAs and must be merged with live state winning on any field that can mutate.
3. **Transcript chunk boundaries lose context** — the transcriber emits chunks every 800ms. A boundary that cuts "my order number is" / "ABC-12345" in half means neither chunk retrieves the order. Both retrieve nothing relevant. Fix: sliding-window overlap (default 200ms tail of previous chunk prepended to the embed query) plus chunk coalescing before embedding.
4. **LLM token budget overflow** — retrieved context plus system prompt plus transcript history pushes the user prompt past the model's window. Most models silently truncate from the middle; some refuse the request. Either way the model is operating on a corrupted prompt. Fix: hard token budget per retrieval surface (default 1500 tokens of context, summarized if over).
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