Sr. Director of Product at Aisera, Jigar brings 15+ years in enterprise AI, GenAI innovation, agentic automation and product-led growth.
Enterprise AI is in the midst of a fundamental shift. The early wave of adoption centered on prompt engineering, crafting clever inputs to coax more useful responses from LLMs. But as enterprises scale AI across domains, a deeper challenge has emerged: Prompts alone are not enough. The new frontier is context engineering—an end-to-end discipline that prepares, updates and governs the information an AI uses before, during and after each step of a workflow.
Context engineering spans retrieval and grounding, memory management, policy and permissions and stepwise context refresh so the system keeps intent, constraints and allowable actions intact. In other words, it’s not just asking the right question; it’s ensuring the AI already has the right, crisp and current information at every step so multi-step workflows reach the goal reliably and efficiently.
Context gives AI the situational awareness needed to act intelligently, but it comes at a cost. LLMs process inputs as tokens, and each token incurs compute costs and latency. Context windows—the model’s “working memory”—are finite. Overloading them with unnecessary or poorly selected data inflates expenses, slows performance and can even reduce accuracy. More context isn’t better; smarter context is.
Why Context Matters More Than Ever
For AI to deliver real business value, it must move beyond generic responses and demonstrate true situational awareness—understanding the user’s environment, history and data context.
Consider the example of IT support. An employee says, “I can’t access my virtual desktop.” A generic chatbot offers basic troubleshooting steps. A context-aware agent recognizes that the employee switched departments last week, detects a permissions mismatch and automatically applies the correct access without escalation.
In each case, the AI’s ability to deliver a high-quality answer depends less on how the question is asked and more on what the system already knows when it hears it.
The Hidden Cost Of Too Much Context
If context is so powerful, why not give the AI everything?
Because every extra token comes with trade-offs in cost, complexity and accuracy. Naively dumping an entire document set or database into a prompt is not only expensive; it risks degrading results.
Key challenges include:
• Latency And Limits: LLMs have finite context windows (from 4K to over 100K tokens). Approaching these limits increases processing time; exceeding them leads to truncation or ignored data.
• Noise And Distraction: Irrelevant or conflicting content can mislead the model, causing hallucinations or off-topic responses—a phenomenon sometimes called context poisoning.
• Scalability And Cost: A 20,000-token prompt costs roughly 10 times more than a 2,000-token prompt and takes longer to process. At enterprise scale, that difference can make deployments economically unsustainable.
Multi-step AI agents call APIs, iterate on their reasoning and retain conversation history. The text they carry forward (the “prompt”) grows rapidly, driving up token costs, slowing responses and diluting accuracy unless that context is continuously curated (summarized, deduplicated and relevance-filtered).
Smarter Context Engineering Strategies
Over the past year, I’ve seen and helped formalize best practices for building lean and structured context pipelines that maximize value while minimizing waste.
1. Utilize selective retrieval. Instead of loading an entire knowledge base into the prompt, retrieve only what’s relevant at query time. If a user asks about an invoice, fetch that invoice, not the whole financial archive. Semantic search and vector embeddings can pinpoint the most relevant passages with high precision.
2. Support domain segmentation. Assign domain-specific context to domain-specific agents: HR agents see HR data, IT agents see system logs, and finance agents see transaction records. This prevents irrelevant context from entering the prompt, improving both efficiency and accuracy
3. Encourage summarization and compression. Long documents, transcripts or conversation histories can be distilled into concise summaries before being sent to the model. Advanced LLMs can even self-summarize intermediate outputs to prevent prompt inflation during multi-turn sessions
4. Leverage external memory. Store intermediate results, user profiles or historical records outside the prompt—in databases or vector stores—so agents can query them on demand. This allows persistence across steps or sessions without keeping all details in active memory
5. Promote tool use instead of raw data. Rather than preloading massive datasets, give AI agents the ability to call APIs or tools. For example, instead of embedding an entire CRM dataset, provide a “lookup customer record” function. The AI fetches and summarizes information only when needed.
When combined, these tactics create a context pipeline: retrieve relevant data, summarize it, store long-term facts externally and leverage tools for on-demand access. The AI sees exactly what it needs—no more, no less.
Why Efficiency Is A Strategic Advantage
These techniques are more than technical optimizations. They’re strategic levers that determine whether AI can scale sustainably by enabling:
• Dramatic Cost Reductions: Intelligent prompt trimming can cut token usage without harming quality. At enterprise scale, measuring the difference can decide whether AI is a core business enabler or a budget drain.
• Faster Performance: Leaner prompts mean quicker response times—often the difference between two seconds and eight. Lower compute loads enable more concurrent users without linearly increasing infrastructure costs
• Improved Accuracy And Trust: By eliminating irrelevant noise, context-optimized agents stay on topic, hallucinate less and handle edge cases more reliably. This builds user trust and supports wider adoption
The Future: Intelligence Meets Efficiency
The future of AI will pair intelligence with efficiency. Context engineering is becoming a distinct discipline beyond prompt design, focused on shaping the information AI systems consume. While larger models with extended context windows will continue to emerge, their potential will remain limited without well-designed, efficient context pipelines.
For enterprises, scaling performance is not only about increasing model size; it also requires improving input quality and managing costs. The next wave of AI advances will come as much from effective context management as from more powerful models. In this way, context engineering is both a performance driver and a sustainability strategy, enabling organizations to scale AI thoughtfully, affordably and responsibly.
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