#119 — Context engineering

Context engineering is the discipline of architecting every bit of information—documents, data, instructions, recent events—fed to a large language model (LLM) during a session. Rather than merely 'prompt engineering,' it ensures models generate reliable, auditable, and business-ready results. Smart startups use context engineering to treat LLMs like analysts, not oracles, significantly improving accuracy, traceability, and user trust. For example, Instacart's product search uses context engineering to blend live shopping data, product specs, and user preferences for personalized results.

LLMs are prone to hallucinations if given ambiguous or incomplete contexts. Rigorous context engineering—injecting up-to-date retrievals, limiting irrelevant noise, enforcing chain-of-thought reasoning, and specifying output formats—dramatically reduces error rates. In legal tech, DoNotPay built an 'AI lawyer' that cross-references case law with user queries, filtering context tightly to avoid risky mistakes during real-world document generation.

Retrieval-Augmented Generation (RAG) pipelines fetch live external data—docs, stats, ratings—to inject as context before LLM inference, ensuring responses always reflect the most recent truth. For instance, Hugging Face’s documentation QA bots dynamically pull fresh docs for every query, giving developers up-to-date answers beyond the model’s training window. Founders should index all key documentation, set up scalable retrievers, and validate injection mechanisms for consistency and relevance.

In multi-agent systems, each agent (retriever, reasoner, critic, safety, output) expects and returns data in precise formats—just like APIs. Define strict contracts for every handoff (JSON schemas, input/output shapes), document protocols, and test with real scenarios. Modular architectures power products like Modular’s AI assistant, where retrievers fetch data, critics assess quality, and synthesizers generate user-facing answers in seamless, composable flows.

Unstructured responses break integrations and risk unpredictable results. Specifying structured output—like strict JSON formats—let founders parse results, trigger downstream automations, and safely process user requests at scale. Klarna’s customer service agent outputs only valid XML to interface with their backend CRM, cutting incident rates and eliminating costly surprises.

Context engineering enables precise personalization using relevant, non-invasive signals: session history, location, recent interactions, and public preferences, sliced and injected as needed. For example, Shopify’s product recommendation bots personalize shopping journeys using only session recency and cart data—no persistent tracking or personal info—proving context can drive personalization ethically and effectively.

Overstuffed context windows can dilute relevance, confuse the model, and trigger token limits—leading to failures and performance drops. Use memory compression, relevance scoring, and selective injection to keep context lean and focused. Case study: A financial planning startup slashed context payloads by 68%, resulting in 2.5x faster response times and improved auditability for compliance reviews.

Yes, frameworks like LangChain, Semantic Kernel, and DSPy offer primitives for RAG, memory management, output formatting, and tool calling. Founders can scaffold context-driven agents, define workflows, and run production-grade experiments with minimal boilerplate. For example, SmartAgent.ai built a pipeline with LangChain to automate 30% of customer queries—entirely through automated context construction and agent orchestration.

Chain-of-thought prompts ask LLMs to explicitly reason through every step before giving answers, exposing intermediate logic and boosting overall reliability. In health tech, Ada Health’s symptom checker requires LLMs to show every reasoning path, allowing doctors to audit AI decisions and catch errors midstream—improving patient safety and regulatory compliance.

First, map out every info source, API, and tool needed for your target task. Next, architect the context window—deciding which docs, data, tools, and memories to inject. Define and document agent handoff contracts, enforce structured output, and iterate with live user sessions, measuring for accuracy and traceability. Real-world example: A SaaS founder replaced ad-hoc prompts with a modular context builder, cutting deploy-time errors by 50% and unlocking scalable customer support from day one.

Context engineering lets founders cut wasted API calls, reduce error rates, and increase product reliability by focusing model attention on only what matters. Companies like Jasper.ai improved their content automation ROI by hyper-optimizing context payloads—resulting in lower costs and better output quality for end users.

Common pitfalls include overstuffing context windows, skipping relevance scoring, ignoring structured outputs, and failing to document agent interfaces. For instance, an edtech startup saw dropout rates spike when context windows were packed with irrelevant course summaries. Fixes included relevance weighting and lean memory injection—restoring product usability and search ranking.

Yes. By compressing memory and optimizing document retrieval, you maintain fast response times. Case: Zapier’s internal bots use context engineering to route only pertinent event histories—enabling sub-second replies while keeping outputs personalized and reliable.

Great context engineering surfaces up-to-date responses, accurate product data, and precise reasoning—all boosting trust and click-through rates on landing pages. SaaS companies using RAG see lower bounce rates; search engines reward clear, accurate, and relevant output—leading to better rankings and user retention.

Limit injected data to session-level or anonymized events, removing PII before context construction. Fintech and healthcare AI providers use context engineering to comply with GDPR and HIPAA by designing privacy-preserving contexts—unlocking advanced personalization without exposing users to compliance risks.

Key metrics include inference accuracy, token efficiency (tokens per useful fact), completion latency, error/incident rates, and downstream automation success. Example: A customer support startup tracked 'context relevance' and saw NPS scores double after context engineering reduced hallucinations and improved solution rates.

Absolutely. By slicing context into agent roles (retriever, critic, operator), you handle negotiation, escalation, and multi-user support. For example, Intercom’s AI support stack allocates context slices to route users, resolve disputes, and escalate issues—all within strict context windows for optimal accuracy.

Yes. Dynamic context builders use rules and relevance models to assemble context per session, tailoring complexity to high-value users or critical tasks. B2B SaaS platforms like Salesforce automate context assembly for tiers of users—executive buyers get business summaries, while admins receive granular setup guides.

It provides an audit trail for every inference—logging injected documents, tool outputs, user signals, and agent handoffs. Regulated startups (insurance, legal, healthcare) use this for traceability, issue review, and compliance reporting, dramatically reducing legal risk and improving regulator trust.

Top picks: LangChain for workflow orchestration, DSPy for prompt/model optimization, Semantic Kernel for agent design, and LlamaIndex for efficient document retrieval. In production, Notion automated 50% of its AI search results using LangChain-powered dynamic context retrieval and modular agents.