Context Engineering

Context Engineering with a Streaming Database

Context engineering requires fresh, structured data for LLM prompts. RisingWave continuously computes and serves contextual data products using SQL — reducing token costs, improving accuracy, and enabling real-time personalization for AI applications.

Start Free Documentation

10-50x

Token Reduction

Pre-aggregated context sends concise summaries instead of raw events, dramatically cutting LLM costs

SQL

Context Products

Define context schemas as materialized views using standard SQL — joins, aggregations, and filters

Real-Time

Personalization

User-level views update from clickstream and behavioral data, reflecting current intent instantly

Incremental

Updates

Context freshness scales independently of data volume — only changed rows trigger recomputation

Why It Matters

What is context engineering and why does data freshness matter?

Context engineering is the discipline of curating the right data for LLM prompts — selecting, aggregating, and structuring information so models produce accurate responses. When context data is stale, models hallucinate, recommend sold-out products, or miss recent user intent, making freshness a critical quality dimension.

Factor	Batch Context	RisingWave Context
Context Freshness	Hours (batch refresh)	Sub-second (streaming)
Token Cost	High (raw data dumps)	Low (pre-aggregated summaries)
Personalization	Static segments	Per-user real-time views
Hallucination Risk	High (stale facts)	Low (current state)

→Prompt quality is bounded by the freshness and relevance of context data
→Pre-aggregated context reduces token costs by 10-50x compared to raw event logs
→Real-time behavioral data enables per-user context reflecting current intent
→Fresh factual context grounds the model in current reality, reducing hallucinations

How It Works

How does RisingWave enable real-time context engineering?

RisingWave ingests streaming data from Kafka, databases, and APIs, then maintains continuously-updated materialized views that serve as context data products. Applications query these views via PostgreSQL protocol to build LLM prompts with always-fresh, pre-structured data — no batch jobs or caching layers needed.

SQL-Defined Context Products

Define context schemas as materialized views using standard SQL — joins, aggregations, window functions, and filters

Incremental Computation

Views update incrementally as source data changes, not on a schedule. Context is always as fresh as the latest event

Multi-Source Enrichment

Join user behavior streams with product catalogs, inventory data, and historical preferences in a single SQL query

Standard PostgreSQL Access

Any application that speaks PostgreSQL can read context views — LangChain, custom APIs, or direct psql queries

Cost + Accuracy

How does fresh context reduce AI costs and improve accuracy?

Fresh, pre-aggregated context reduces token usage by sending concise summaries instead of raw data dumps. It simultaneously improves accuracy by grounding LLM reasoning in current state — meaning fewer retries, fewer hallucinations, and higher user satisfaction per API call.

→Pre-computed aggregations reduce prompt token count by 10-50x compared to sending raw event logs
→Fresh inventory and pricing data eliminates recommendations for unavailable products
→Real-time session context enables mid-conversation personalization without re-fetching user history
→Structured SQL output maps directly to prompt templates, eliminating parsing and transformation code
→Incremental updates mean context freshness scales independently of data volume

Frequently Asked Questions

What is context engineering for AI applications?

How does RisingWave reduce LLM token costs?

Can RisingWave handle real-time personalization context?

How does context freshness affect AI accuracy?

Ready to start context engineering?

Build always-fresh context products for your AI applications with SQL.

Start Context Engineering