Thanks — this is a great question and a common design decision when building conversational agents that must return short, highly relevant clips. I’m responding to your question about which approach is simpler, more efficient, and more accurate: (A) give the LLM the entire JSON of clips in the system prompt and let it pick, or (B) run a retrieval step to pick the most relevant clip(s) and give only those to the LLM. Short answer • Use a retrieval-first approach (B): retrieve the most relevant clip(s) and then pass those to the LLM. It’s simpler to scale, more efficient, and more accurate for pinpointing precise clips. Why retrieval-first is generally better • Efficiency: Sending only a small set of candidate clips keeps token usage low and latency lower than giving the whole dataset to the model each turn. • Accuracy: A dedicated retriever (embedding-based or keyword-based) is much better at surface-level similarity and ensures the LLM is working from the most relevant candidates. The LLM then focuses on interpretation and response, not search. • Scalability: As your clip dataset grows, the system prompt approach becomes impractical (token limits, slow inference). Retrieval scales linearly in storage and log-time (with an index). • Determinism & Auditing: Retrieval results are explicit and reproducible; you can inspect why a clip was chosen. Letting an LLM “choose” from raw JSON can be opaque and inconsistent. Practical architecture (recommended) 1. Preprocessing / Indexing ◦ For each clip, compute a semantic embedding of the clip metadata (description + optionally a short transcript excerpt). ◦ Store embeddings with clip metadata (id, start, end, description, confidence, etc.) in a vector store (e.g., Pinecone, Milvus, Weaviate, or a simple FAISS index). 1. Query-time retrieval ◦ For each user utterance (or the user’s explicit retrieval query), compute an embedding and run a nearest-neighbor search against the vector index. ◦ Retrieve top-K candidates (K = 3–10) and include relevant metadata and similarity scores. 1. Reranking / Filtering (optional but improves precision) ◦ Apply a fast semantic or lexical reranker on the top-K results (BM25, cross-encoder model, or an LLM reranker) to improve ordering. ◦ Filter by domain rules (length constraints, recency, content flags). 1. LLM step ◦ Pass the top 1–3 retrieved clips (or a concise merged prompt of them) plus the conversation context to the LLM. ◦ Use the LLM to select the single best clip, produce a human-friendly snippet, or generate dialogue referencing those clips. ◦ Keep the LLM prompt constrained: include only necessary context, clip metadata, and an explicit instruction on selection criteria (relevance, specificity, timestamps). 1. Post-processing ◦ Optionally run a final verification (e.g., check that returned timestamps exist, confirm non-sensitive content). ◦ Return clip id + timestamps + the natural-language text to the frontend. Concrete tips to maximize accuracy • Use embeddings tuned to your modality: if you have transcripts, embed the transcript + description. If only descriptions exist, use those. • Normalize descriptions: trim, remove stopwords if noisy, and include important metadata as structured fields. • Combine semantic and lexical signals: semantic retrieval finds concept matches; lexical (BM25) captures exact keyword matches which can be critical for short clip queries. • Use reranking with a lightweight cross-encoder for highest precision on top candidates. • Keep K small for LLM consumption but large enough for recall. Typical K values: 3 for tight precision, 5–10 if you expect diverse phrasing. • Prompt the LLM with explicit selection rules: e.g., “Choose the single clip that directly answers the user’s question. Prefer exact matches to entities and time-specific requests.” • Measure and iterate: log retrievals, similarity scores, and final chosen clips. Compute precision@1 and recall metrics for continuous tuning. When “give-all-to-LLM” might still work • Very small datasets (dozens of clips) where token limits aren’t a problem. • Prototypes where speed of iteration matters and you want to test conversational behavior before building an index. • But even here, retrieval-first will be better for production. If you want, I can help with any of the following: • A concrete end-to-end prompt template for the LLM given top-K clips. • Example code snippets for embedding + vector search and how to format the retrieved clip objects for the LLM. • Guidance on which similarity thresholds and K to start with based on dataset size. Which of those would you like next? Also, do you have transcripts for clips or only descriptions? That will affect the embedding strategy. , with interactive elements