Jafar Najafov's Eight Techniques of Context Engineering

On October 13, Jafar Najafov systematically argued in his "context engineering" series that engineers at Anthropic, OpenAI, and Google get 10x better results with the same large models—the key has shifted from prompt engineering to context engineering, i.e., what kind of context you construct for the model. The series has eight parts, each covering context construction methods for different scenarios.

Confirmed

• Technique 1, "role + expertise context": LLMs don't just need instructions—they need to "become" someone; a persona like 'senior developer' activates entirely different reasoning modes, with fundamentally different code review output.
• Technique 2, "reference example context": giving the model 2-3 examples of the target style and spelling out the patterns reduces hallucinations and keeps organizations consistent across hundreds of outputs.
• Technique 3, "constraint context": the core claim is "unlimited freedom = mediocre output"; explicitly stating "what not to do" forces the model to focus and deliver actionable solutions.
• Technique 4 targets image generation (Nano Banana Pro, Image gen 1.5, etc.): image models are trained on photographers' and artists' language; "beautiful sunset" only triggers stock-photo junk, so use technical parameters like camera and lighting instead.
• Technique 5, "audience context": when writing copy, provide a specific audience persona (e.g., "an exhausted founder who has tried every productivity tool") and fill in seven elements including demographics to activate the right language patterns and pain points.
• Technique 6 targets video generation (Veo 3, Runway, Kling): replace descriptive language with director-level shot language, using templates covering seven dimensions including shot size.
• Technique 7, "chain-of-thought context": citing Google research, the same model's accuracy jumped from 18% to 57% when forced to reason step by step; the author provides a six-step reasoning template.
• Technique 8, "output format context": LLMs are pattern-completion machines; explicitly specifying the expected output structure section by section eliminates roughly 90% of the "that's not what I meant" rework.

Why It Matters

The series consolidates scattered prompting tips into a reusable template system spanning text, code, image, and video generation, backed by concrete numbers (18%→57%) demonstrating the payoff of structured context—ready for content teams and developers to apply directly.

2026-10-13 ~ 2026-10-13 · 9 related posts

• [source] Context engineering, not prompt engineering: 8 techniques for 10x LLM output — JafarNajafov · 2026-10-13
• Persona + expertise prompting turns generic reviews into senior-engineer code audits — JafarNajafov · 2026-10-13
• Reference-context prompting: paste examples and name the patterns for precise output — JafarNajafov · 2026-10-13
• [source] Constraint-context prompting: telling the LLM what NOT to do yields actionable output — JafarNajafov · 2026-10-13
• Image prompting: camera, lens, and lighting params beat 'beautiful sunset' for Nano Banana Pro — JafarNajafov · 2026-10-13
• Prompt tip: a seven-field audience profile makes AI copy speak your reader's language — JafarNajafov · 2026-10-13
• Video gen prompting: director-level shot language beats descriptions for Veo 3, Runway, Kling — JafarNajafov · 2026-10-13
• [source] Google research: forcing step-by-step reasoning lifts same-model accuracy from 18% to 57% — JafarNajafov · 2026-10-13
• Prompt tip: define the exact output format first to kill 90% of 'that's not what I meant' moments — JafarNajafov · 2026-10-13