PromptOps
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Track 4
4. High-Performance Context Engineering
0 / 19 lessons
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1
Decision-Oriented Summarization
Extract signal for a specific decision
Intermediate
2
Audience Translation
Same content, completely different world
Intermediate
3
Long-Context Prompts That Don't Get Lost
A million tokens in, one relevant needle out
Intermediate
4
Deep-Research Prompts
Brief it like an analyst, not a search box
Intermediate
5
RAG & Document Grounding
Stop hallucinations with real data
Advanced
6
Cost Optimization & Token Economics
Same output. 99% less spend.
Advanced
7
Context Engineering
The LLM is the CPU. Context is RAM. You are the OS.
Advanced
8
Cache-Aware Prompt Design
Order your prompt like a cache, not an essay
Advanced
9
Output-Constrained Decoding
Prevent parsing failures at the compiler level.
Expert
10
Dynamic Few-Shot Selection
Choose the best examples dynamically at runtime.
Expert
11
Context Windows & Caching Strategies
Optimize memory retrieval to minimize token cost and latency.
Expert
12
RAG Prompt Optimization
Engineer context prompts to minimize hallucination in vector search systems.
Expert
13
Multi-Lingual Calibration
Design prompts that maintain logic and formatting across diverse languages.
Expert
14
Prompt Compressors & LLMLingua
Compress prompts to remove redundant tokens while maintaining task performance.
Expert
15
Token Auditing & Optimization
Audit prompt tokens to optimize cost and prevent semantic degradation.
Expert
16
Multi-Modal Context Injection
Structure prompts to reference images, video, and audio contexts.
Expert
17
CoT Decoding Controls
Enforce exact formatting limits on reasoning steps to minimize cost.
Expert
18
Context Retrieval Sorting
Order context documents to avoid model recall degradation.
Expert
19
Context Window Chunking
Process document libraries that exceed context windows using sliding chunk schemas.
Expert
4. High-Performance Context Engineering