Cut LLM token costs by pruning irrelevant context dynamically

Many engineering teams, platform groups, and companies running assistant or RAG workflows are seeing token bills climb as prompts include large amounts of retrieved or historical context; with an estimated 2 million software teams and a $3K ACV average (total addressable tooling + cost-savings market of about $6.0B), token inefficiency is a tangible line-item pain. The problem is not just cost—irrelevant or stale context also increases latency and can degrade model outputs when models are forced to consider noisy inputs. A practical product would be a lightweight middleware plus SDK and IDE/CI plugins that dynamically prunes retrieved and conversational context to meet an adaptive token budget using a mix of fast heuristics (recency, type filters, semantic similarity thresholds) and learned rankers, with fallbacks and per-request cost/quality controls; it should expose telemetry showing per-call token and dollar savings and integrate with popular retrievers and vector stores. This is compelling now because LLM adoption in developer workflows is accelerating, retrieval-augmented architectures make the retrieval-vs-model-cost tradeoff explicit, and developers expect seamless plugins—giving a clear commercialization path to the $3K ACV target via tooling subscriptions and measurable cost reduction. To stand out you would emphasize deterministic, auditable pruning policies, per-tenant tuning, tight local/hybrid IDE integrations, and an ROI dashboard that proves 20–50% token savings on representative workloads. Be honest that risks include potential quality regressions from over-pruning, significant integration work across retrievers and models, and the need for strong case studies to close sales; a focused MVP with 50 pilot teams and clear saving metrics would be the fastest way to validate unit economics and product–market fit.