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DeepHub IMBA
DeepHub IMBA
Jun 1, 2026 · Artificial Intelligence

The Essence of Prompt Engineering: Roles, Tasks, Context, Format, and Constraints

Prompt engineering designs inputs for large language models by combining clear intent, relevant context, explicit format, and constraints, turning ambiguous queries into reliable, high‑quality outputs through a structured, iterative process illustrated with concrete examples and advanced techniques.

AI communicationChain-of-ThoughtLLM reliability
0 likes · 23 min read
The Essence of Prompt Engineering: Roles, Tasks, Context, Format, and Constraints
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 31, 2026 · Artificial Intelligence

Why Agent Reliability Needs More Than Bigger Models: Lessons from Harness Engineering

The article argues that the reliability of large‑model agents cannot be solved by scaling models or extending context windows; instead, a stable, auditable, and rollback‑capable runtime—what the author calls a State‑Aware Runtime—is essential for long‑term, industrial‑grade agent systems.

AgentHarness EngineeringLLM reliability
0 likes · 13 min read
Why Agent Reliability Needs More Than Bigger Models: Lessons from Harness Engineering
inShocking
inShocking
Apr 23, 2026 · Artificial Intelligence

From Chatty to Capable: Key Challenges and Solutions for Deploying AI Agents in Production

The article identifies five often‑overlooked engineering pitfalls—unstable model output, fragile tool chains, memory loss, multi‑tenant interference, and uncontrolled autonomy—and provides concrete validation, tool‑tiering, external memory, isolation, and risk‑based execution strategies to reliably move AI agents from demo to production.

AI agentsLLM reliabilityMulti‑Tenant Isolation
0 likes · 11 min read
From Chatty to Capable: Key Challenges and Solutions for Deploying AI Agents in Production
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 19, 2026 · Artificial Intelligence

Making LLM Answers Trustworthy: Citation Attribution and Hallucination Detection

This article explains why simple prompt‑based citation is insufficient for Retrieval‑Augmented Generation, introduces a sentence‑level attribution pipeline, combines semantic similarity with NLI verification, and presents practical hallucination detection and structured JSON output to ensure answer reliability.

LLM reliabilityNLIPrompt Engineering
0 likes · 10 min read
Making LLM Answers Trustworthy: Citation Attribution and Hallucination Detection
Tencent Cloud Developer
Tencent Cloud Developer
Oct 15, 2025 · Artificial Intelligence

Why LLMs Are Unreliable: The pⁿ Dilemma and Building Trustworthy AI‑Human Collaboration

The article explains that large language models are fundamentally probabilistic predictors, causing their success rate to drop exponentially with task complexity (the pⁿ dilemma), and proposes a systematic, human‑centered approach—using deterministic tools, narrowing prompt scope, and delivering incremental results—to create reliable AI‑human collaborative systems.

AI-human collaborationLLM reliabilityPrompt Engineering
0 likes · 66 min read
Why LLMs Are Unreliable: The pⁿ Dilemma and Building Trustworthy AI‑Human Collaboration