What is Context Engineering?
Context engineering is the discipline of shaping everything that surrounds the model call: system prompts, retrieval, tool schemas, and guardrails. It’s how you turn LLMs into dependable business systems.
How does Context Engineering work?
Select the right context sources (RAG), design chunking and metadata, craft prompts and schemas, and evaluate outputs for faithfulness. Add tools and rules so the model can act safely.
When should you use it? (Typical use cases)
- RAG assistants with high factual accuracy.
- Agents that make safe, auditable changes in systems.
- Analytics narratives grounded in governed datasets.
- Multilingual support based on curated knowledge.
Benefits and risks
Benefits
- Higher answer quality.
- Lower hallucination risk.
- Repeatable behavior across tasks.
Common pitfalls/risks
- Overly complex pipelines.
- Context bloat inflating costs.
Antire and Context Engineering
Antire delivers context-first designs: retrieval quality, prompt patterns, tool governance, and evaluation, so AI apps are useful, safe, and economical.
Services
- Data platforms and applied AI
- Tailored AI & ML
- Cloud-native business applications
- Fast Track AI Value Sprint