Original: My Complete Understanding of AI Programming in 2025 — Part 1 - Loongphy’s Blog
LINUX DO repost: @MineMine
Core Understanding #
1. AI Coding Paradigm Overview: Shift the delivery focus from “lines of code written” to “task definition quality, context organization quality, and acceptance loop quality.” Define problem boundaries and success criteria first, then let models participate in implementation — this significantly improves stability and reusability.
2. How LLMs Work (Probabilistic Continuation): Models fundamentally generate high-probability outputs based on context. Their strengths are pattern induction and structural completion; their weaknesses are factual memory and long-term consistency. Treating models as “high-dimensional pattern matchers” is most effective — input quality and context structure directly determine the output ceiling. Constraints, fact sources, and prohibitions must be explicitly stated in the input.
3. Prompt Evolution: Effective prompts no longer pursue “fancy incantations” but emphasize task specification (goals, inputs, constraints, output format, acceptance criteria), shifting models from “guessing what you want” to “executing by contract.”
Mindset Upgrade & Role Transformation #
4. Simplicity (Reduce Guidance): Use short instructions + clear goals + necessary context. Avoid verbose role-playing — let the token budget prioritize the problem itself rather than the “performance layer.”
5. Identity Upgrade: Treat AI as a “collaborative executor” rather than a “chat companion.” Drive efficiency through task assignment, result checking, and iterative correction — this significantly reduces unproductive back-and-forth.
8. Focus & Thinking Ability: The scarce skills in the AI era are problem decomposition, boundary definition, risk anticipation, and retrospective abstraction. Shift human effort from “line-by-line coding” to “problem definition and boundary control” — core productivity comes from decomposition, judgment, and retrospective abilities.
12. Mindset Shift (From Author to Director): The role shifts from “writing every line of code personally” to “setting rules, assigning tasks, supervising quality, integrating outputs, and reviewing results.” The focus moves from local implementation to global quality and rhythm control. This is the most critical capability migration in the AI coding era.
Model Selection & Evaluation #
6. Open Source vs. Closed Source: Selection follows task constraints rather than ideological preference. For complex engineering and high-stability scenarios, prioritize capable models with mature toolchains. For local deployment and cost-sensitive scenarios, prioritize controllable open-source solutions. Ultimately decide by task pass rate and total cost.
7. Stay Away from Marketing Narratives: Evaluate models by real task pass rates, change quality, regression risk, and total cost — not by leaderboard hype or short demos.
Workflow Methodology #
9. Files as Memory (External Context): Accumulate long-term memory through AGENTS.md, PLANS.md, and specification documents to reduce session drift, repeated explanations, and cross-turn information loss.
10. Planning Before Execution: Form an executable plan first (steps, dependencies, acceptance criteria, rollback, etc.), then begin implementation. Moving error prevention to the design phase significantly reduces late-stage rework and the hidden cost of “discovering the wrong direction after finishing.”
11. Results as Standard (Verifiable Delivery): All outputs should lead to verifiable results (runnable, testable, explainable, reversible). Replace “looks correct” with “complete evidence chain” — use tests and acceptance checklists as the final verdict.
13. Avoiding Bad Practices: Avoid pushing through long sessions, blind parallelism, frequent model switching, and ignoring context compression. Adopt small-step commits, phase summaries, periodic context resets, and milestone acceptance to significantly reduce the probability of losing control.
Practice & Tools #
14. Codex Best Practices: High-quality practice depends on: clear task decomposition, explicit constraints, command-driven workflows, mid-point checkpoints, final acceptance and retrospective — crystallize every successful experience into repeatable templates like prompts and skills.
15. Product Recommendations: Use Amp Code, v0, ChatGPT, Grok, and Gemini in combination by capability (code execution, frontend generation, search integration, deep reasoning, multimodal). Achieve stable output with “primary tool + backup tool + unified acceptance standard.”