Context representation and storage are foundational for enabling AI systems to maintain coherent interactions across multiple turns. The video explains that context can be captured as a sequence of embeddings, structured graphs, or hierarchical memory slots, each balancing fidelity and computational cost. Token‑level embeddings preserve fine‑grained details but grow linearly with dialogue length, prompting techniques like sliding windows, summarization, or attention‑sparse mechanisms. Graph‑based representations encode relationships between entities, intents, and discourse markers, allowing efficient retrieval of relevant sub‑contexts. Hierarchical memory organizes information into short‑term buffers for immediate relevance and long‑term stores for background knowledge, often using key‑value stores with learned retrieval functions. The talk also covers serialization formats—JSON, protobuf, and binary tensors—and the trade‑offs between human readability and speed. Compression methods such as quantization, pruning, and vector‑product quantization reduce storage footprints while preserving semantic similarity. Finally, the video highlights best practices: regularly purge stale entries, align context windows with model context limits, and employ caching layers to minimize latency in real‑time applications.