prompt engineering
1183 articles · 15 co-occurring · 10 contradictions · 135 briefs
This entire page is part of IBM's prompt engineering guide and is structured around prompt engineering techniques.
Implicitly argues that better prompts are less important than orchestration structure; the value is in architectural clarity not prompt crafting
Author implicitly argues that better prompts won't solve this—you need externalized context structure. Prompting each agent to 'use the right table' fails because definitions aren't shared.
Article explicitly says fixing multi-agent failures 'does not require tweaking agent prompt topologies'—argues architectural clarity matters more than prompt optimization.
[INFERRED] "the most basic concepts become unintelligible because of its AI mannerisms" — User reports that Claude's writing style and mannerisms (influenced by prompting/instruction-following) make basic concepts unintelligible, suggesting current prompting strategies produce undesirable stylistic outputs.
The research suggests learned harness policies beat hand-tuned static prompts, implying that dynamic context management (harness annealing) outperforms traditional prompt engineering approaches.
Author frames issue as not 'special harness sauce' (prompt tricks) but fundamental context strategy, suggesting context architecture > prompt optimization
[user-report] "I'm finding Claude 5 Opus pretty annoying to talk to. It makes a ton of assumptions so I'm constantly correcting it." — User reports that Claude Opus makes excessive assumptions, contradicting the ideal of precise instruction-following behavior
Article implicitly rejects prompt-engineering-centric thinking ('not really about prompting anymore'). The leverage point is context assembly, not instruction crafting.
The article explicitly argues AGAINST iterative prompt refinement ('slot machine') in favor of specification-first workflows. This challenges the conventional prompt-engineering wisdom.
[INFERRED] "viral "anti-slop" markdown files for agents and realizing it is basically a 100% AI written" — Article warns that AI-generated instruction files paradoxically create worse outputs, contradicting the assumption that such files improve system behavior
This entire page is part of IBM's prompt engineering guide and is structured around prompt engineering techniques.
Prompt engineering is the process of structuring inputs, and it has emerged as a crucial technique for maximizing the utility and accuracy of these models" — Direct definition and articulation of prom
Instead of creating the perfect prompt, context engineering focuses on optimizing both the prompt and everything around the prompt." — Article explicitly positions context engineering as the evolution
clever prompts represented perhaps 0.1% of the total context modern AI systems process" — Article directly challenges prompt engineering as the primary driver of AI effectiveness in production. Contex
Context engineering combines prompt engineering, retrieval-augmented generation (RAG), and multi-agent techniques into one system, instead of using them separately." — Article explicitly names prompt
Over the past couple of years, building applications with large language models (LLMs) has shifted focus from prompt engineering to context engineering. In early LLM applications, users spent time cra
是一套面向 AI 行为工程的提示设计方法论,用最小的上下文开销,换取最可靠的行为改变" — agent-skills defines a prompt design methodology that minimizes context overhead while maximizing behavioral reliability — extends prompt engineering with
Explicitly mentioned as Chapter 3 of the curriculum; prompt engineering is foundational to context design
[DIRECT] "Putting "no eyebrows" in my CLAUDE.md" — Author uses CLAUDE.md configuration file to specify behavioral modifications (persona traits like eyebrows), demonstrating practical system prompt us
Layer around the model: defense is a system layer, not fine-tuning, modeled on software security." — Article explicitly describes defense as system-layer architecture, modeled on software security pri
you can get indirect prompt injection to ~0 on unseen attacks if you stack enough layers (model training + input probes + a classifier checking intent)" — Directly describes a proven multi-layered def
Reframes prompt engineering from 'writing longer prompts' to 'structuring information optimally for the model's representation space.'
The codebase is part of the prompt." — Frames codebase itself as essential prompt context, extending conventional prompt engineering to include code state as a first-class prompt component.
check every hypothesis against the recorded history in python before spending actions on it. additionally, every submitted action must carry an expected result, so a missed prediction cancels the rest
Every prompt must: Restate the problem exactly, Define what counts as a full solution, Explicitly ban weaker intermediate results, List traps & edge cases, Force independent adversarial agents to atta
As the models have gotten smarter, they need less direction, fewer constraints, and fewer examples." — Article documents Anthropic's strategic shift in prompt design: reducing system prompts by 80% as
As the models have gotten smarter, they need less direction, fewer constraints, and fewer examples." — Article demonstrates that modern LLMs require less explicit direction in system prompts; as model
"one entire C. elegans" is at the very top with almost 100% of images with a single mask at all developmental ages... "one whole C. elegans" is at THE VERY BOTTOM with only 4-25% of images having a si
The core problem is optimizing prompts to guide model behavior; demonstrates that small prompt variations cause large output differences
Reframes prompt engineering as context distribution problem rather than prompt verbosity problem
"像设计系统一样写 prompt"的方法论" — Article presents a systematic methodology for prompt engineering using design-system principles, introducing constraints-card approach with FONT/STYLE/MODE parameters
The practice of structuring prompts for better results is fundamental prompt engineering, but elevated here to a meta-level: the structure of how you introduce context matters more than the quality of
[direct] "When a better AI model gets released, the first thing you should try is to REMOVE instructions." — Article challenges conventional prompt engineering wisdom by advocating for FEWER instructi
The GCOB framework is a structured prompt engineering methodology
Author's 'clear stop points' are prompt-level engineering patterns that directly control context consumption
Article explicitly distinguishes context engineering (complete information environment) from prompt engineering (single-turn phrasing). Frames context engineering as superset addressing production req
Article explicitly identifies prompt engineering as the 2023 era and establishes context engineering as its successor, creating clear conceptual lineage
Markdown gives you headings, code blocks, bold/italic text, and organized lists. This structure helps AI (and us) quickly find sections without having to read the entire file top to bottom." — Article
Context Engineering is the new big thing for AI coding - not because the concept is new, but because we're finally reaching the point where everyone realizes that putting in the effort to provide exte
Prompts are great for one-off requests and human-in-the-loop interfaces, but terrible for defining the behaviors of systems" — Article directly challenges the applicability of prompts for system-level
Article treats prompt engineering (persona + instructions) as one component of broader context engineering discipline, positioning context engineering as the superset that includes data, state, retrie
Article explicitly identifies prompt engineering as critical skill for multi-agent design; context/role/persona defined via prompts determine agent behavior.
Article explicitly positions context engineering as distinct from and broader than prompt engineering - a key boundary definition. Context engineering encompasses the environment; prompt engineering i
Article explicitly frames context engineering as evolution/progression from prompt engineering, showing how the earlier concept remains valid but becomes secondary to context design.
Article explicitly positions context engineering as the evolution of prompt engineering, incorporating prompt strategies but expanding to holistic token management
[direct] "a leitwort in AI is any word or phrase you use that appears in the agents' thinking traces and guides its behavior" — Introduces leitwörter as a novel prompt engineering technique borrowed f
the questions they ask" — Article directly states that expert users get more value from Claude because of the quality of questions they ask, which is the core of prompt engineering skill.
Article explicitly frames context engineering as evolution beyond prompt engineering, positioning the latter as 'battle against complexity'
Jordan Crawford audited 810 prompts he typed into Claude Code over 17 days to understand how he interacts with the AI" — Article documents a real-world prompt engineering practice with quantified metr
Session explicitly covers 'automated prompt development' as a context engineering lever. Prompt engineering is a foundational CE technique.
Author explicitly identifies prompt engineering as the turning point from failure to success in 2024→2025
Setting a role in the system prompt anchors tone and persona. Anthropic's prompt-engineering guidance is especially clear that role + clear instruction structure beats clever wording." — Article expli
Article explicitly positions context engineering as a replacement/displacement of prompt engineering, arguing that prompt tuning alone fails at scale for stateful systems with tools and agents.
The article demonstrates specific prompt design (adversarial framing) as a context engineering technique
2025: Ask AI better" — Article explicitly identifies prompting/prompt engineering as the 2025 competitive advantage in AI interaction
Context Engineering, a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs" — Survey explicitly positions context engineeri
Episode explicitly distinguishes context engineering from prompt engineering, treating them as separate disciplines with different focuses and skills
writing prompts (prompt engineering is just a small piece of it)" — Directly positions prompt engineering as subset of broader context engineering discipline, adding novel dimension that context encom
[direct] "I really like the term 'context engineering' over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LL
GEPA extends static prompt engineering into dynamic, feedback-driven prompt evolution. Shifts from 'craft good prompt' to 'system that improves prompts iteratively'.
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