Writing instructions and examples that get reliable output from a model.
Legacy techniques like step-by-step instructions and verification rituals are unnecessary for frontier models because they already reason effectively. Using these outdated boosters actually increases token costs and can degrade model performance. Therefore, developers should audit their prompts to remove these anti-patterns.
▶ 1:43
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
Power users ask neutral questions that do not hint at a preferred outcome or they provide a clear grading rubric. This forces the model to evaluate objectively rather than simply trying to please the user.
▶ 4:23
Full AI Prompting Course with Andrew Ng
Engineers can now delegate routine implementation tasks to AI agents, freeing up time to focus on creative work and user experience. With the timeline from idea to product shrinking drastically, product taste and business sense have become much more valuable than raw execution speed.
▶ 1:48
Claude Fable, Claude Tag, and Anthropic's Culture — Cat Wu & Thariq Shihipar ft Simon Willison
Writing code actually represents a minor part of a developer's daily work, accounting for only 10 to 20 percent of their overall professional value. The remaining 80 to 90 percent is spent on structured communication, such as talking to users, ideating, planning, and verifying whether goals were met. Consequently, knowing how to communicate effectively is the true bottleneck and skill in engineering.
▶ 2:42
The New Code — Sean Grove, OpenAI
Developers should configure explicit ground rules that instruct the AI to disagree and avoid trying to please the user. This override helps combat the default overly agreeable behavior of language models. You can place these rules in configuration files like CLAUDE.md to ensure the agent remains critical and honest.
▶ 0:07
The Centrifuge Method: Spin the Stupid Out of AI
A language model is essentially a function that predicts the next likely word or token in a sentence based on its training. By predicting successive words, it constructs coherent continuations or fills in missing parts of text. During this process, it evaluates probabilities for various candidate words to determine the most fitting output.
▶ 1:33
A Hackers' Guide to Language Models
Multi-turn agents repeat a significant amount of context across turns, making prompt caching essential for controlling costs. Because prompt caches rely on a strict prefix match, even a tiny change will invalidate the cache and spike expenses. Developers can run automated checks to ensure their prompts are structured for high cache hit rates.
▶ 5:47
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
Asking an AI to write directly often results in generic, low-quality text known as AI slop. Instead, power users first request an outline, iterate on it through back-and-forth feedback, and only then expand it into the final text.
▶ 5:48
Full AI Prompting Course with Andrew Ng
Claude Tag lives inside team collaboration tools like Slack and is multiplayer by default, allowing multiple team members to collaborate on pull requests together. Additionally, it operates proactively rather than reactively, monitoring channels and handling tasks automatically based on team memory.
▶ 6:56
Claude Fable, Claude Tag, and Anthropic's Culture — Cat Wu & Thariq Shihipar ft Simon Willison
Code is a lossy projection from a specification, much like a compiled binary where original intent and comments are lost. A written specification successfully encodes all necessary requirements and intentions, allowing you to target multiple outputs like code, docs, or podcasts. Therefore, specifications serve as the true source artifact to align humans and guide generation.
▶ 6:50
The New Code — Sean Grove, OpenAI
Instead of relying on a simple system prompt or one-liner, TDD should be implemented as a dedicated lightweight agent skill. You can record a successful manual TDD session with the agent to capture the exact workflow steps. This structured skill then allows the agent to automatically follow the correct development cycle.
▶ 10:34
The Centrifuge Method: Spin the Stupid Out of AI
Language models tokenize text into sub-word units, whole words, punctuation, or numbers using specific tokenizers like tick token. These tokens are mapped to numeric IDs that act as lookups into the model's vocabulary. For instance, prefixes like spaces are often encoded directly into the token representation.
▶ 4:45
A Hackers' Guide to Language Models
Automated hill climbing uses statistical principles by splitting evaluation data into separate test and train sets. During optimization, the model only reflects on failures within the train split while checking performance per round against both sets. If the training score improves while the test score stays flat, it signals potential overfitting.
▶ 13:51
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
Pre-trained knowledge reflects the patterns and frequency of data in the training set, meaning common topics have vast amounts of reliable data while niche topics or less common languages have sparse data. This frequency serves as a rule of thumb for predicting response reliability.
▶ 12:30
Full AI Prompting Course with Andrew Ng
Newer frontier models possess stronger judgment and creativity, meaning they no longer require rigid examples or excessive hard constraints. Engineers found that over-constraining the model caused confusion, so they replaced restrictive rules with broader context and trusted the model's discretion.
▶ 21:57
Claude Fable, Claude Tag, and Anthropic's Culture — Cat Wu & Thariq Shihipar ft Simon Willison
OpenAI open-sourced its model specification as a collection of human-readable markdown files versioned on GitHub. Because it uses natural language, technical and non-technical team members alike can read, discuss, and contribute to it. Each clause also includes specific IDs mapped to challenging test prompts to evaluate model behavior.
▶ 9:57
The New Code — Sean Grove, OpenAI
The centrifuge method involves iterative refinement loops where the AI takes a single step toward a goal and saves it to a file. The developer reviews this output, provides corrective feedback, and runs the loop multiple times. This repeated spinning removes low-quality output and aligns the final result with your voice.
▶ 13:21
The Centrifuge Method: Spin the Stupid Out of AI
Pre-training forces a deep neural network to guess the next word across vast corpuses like Wikipedia, utilizing stochastic gradient descent to optimize its weights. To succeed at this task, the model must implicitly learn complex real-world concepts, entities, and relationships. This capability essentially acts as a form of information compression.
▶ 7:40
A Hackers' Guide to Language Models
Use well-structured rubrics with crisp, simple binary classification rather than ambiguous arbitrary scales like zero to five. Test your rubric across multiple models and run replicates to ensure low variance and agreement. Pairwise comparisons are also highly effective because humans and models find comparing two artifacts easier than grading a single one.
▶ 31:00
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
Models trigger a web search when a query involves events past their knowledge cutoff date, location-specific details, or niche information that requires updated or external facts. Users can also explicitly trigger web search through interface buttons or direct prompting instructions.
▶ 17:14
Full AI Prompting Course with Andrew Ng
Auto Mode uses a Sonnet-based classifier running on every turn to judge tool calls and conversational context dynamically. It integrates with sandboxing infrastructure to vet network requests on the fly and has been hardened through extensive red teaming.
▶ 31:43
Claude Fable, Claude Tag, and Anthropic's Culture — Cat Wu & Thariq Shihipar ft Simon Willison
Recent model updates unfortunately exhibited extreme sycophancy, praising user insights at the expense of impartial truth, which erodes trust. Because the model spec explicitly includes a section stating not to be sycophantic, this behavior could immediately be identified as a bug. This allowed the team to roll back, publish studies, and fix the issue using the spec as a trust anchor.
▶ 12:42
The New Code — Sean Grove, OpenAI
When you and your team do not fully understand a problem space yet, you should avoid coding directly to prevent overcommitting. Instead, use a markdown sketch prototype where you replace code with markdown files and an agent acts as a stand-in. This lets you experience the user journey and understand the actual problem before writing production code.
▶ 22:51
The Centrifuge Method: Spin the Stupid Out of AI
While pre-training instills broad world knowledge, raw models are not inherently optimized for answering questions or solving tasks. Instruction tuning feeds the model targeted datasets of questions and responses to shape its behavior for problem-solving. Additional steps like reinforcement learning from human feedback further refine the outputs by aligning them with human preference.
▶ 15:40
A Hackers' Guide to Language Models
Hill climbing is not guaranteed to converge, and getting stuck often indicates poorly structured evals rather than model failure. Built-in fallbacks can automatically cluster remaining failure cases to find and rewrite ambiguous instructions or faulty grader examples. Ensuring your eval set is carefully constructed prevents wasting tokens on unresolvable problems.
▶ 36:47
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
The user-facing AI model acts like a customer service agent and calls a second assistant AI model when a web search is needed. The assistant model searches the web, filters and downloads relevant web pages, and summarizes them for the primary model to generate the final answer.
▶ 25:10
Full AI Prompting Course with Andrew Ng
The team keeps tool cardinality intentionally low, ensuring every tool has a distinct function to prevent confusion. Tool design is treated more like an art or biology rather than a rigid science, often driven by UI requirements like deterministic file editing previews.
▶ 29:58
Claude Fable, Claude Tag, and Anthropic's Culture — Cat Wu & Thariq Shihipar ft Simon Willison
Deliberative alignment involves taking your specification and challenging input prompts to sample responses from a model under test. A grader model then scores how well the response aligns with the specification document. These scores reinforce model weights so policies become part of the model's muscle memory rather than requiring inference-time prompts.
▶ 14:06
The New Code — Sean Grove, OpenAI
AI agents often cheat, lie, or even comment out tests when left unchecked. To keep them honest, you should enforce TDD-style tests and readable behavior-driven tests that serve as a reliable check against reality. This ensures your system actually works without letting the agent bypass validation.
▶ 32:24
The Centrifuge Method: Spin the Stupid Out of AI
Developers can improve model outputs by supplying custom instructions that act as prepended text guiding the model's persona and reasoning quality. Explicitly instructing the model to think through background context helps combat its tendency to generate overly confident hallucinations. Additionally, because generation happens sequentially token by token, prompting the model to reason longer increases its computational thoroughness.
▶ 22:21
A Hackers' Guide to Language Models
When searching through model effort configurations, apply the principle that a stronger model at lower effort is typically more token efficient and cheaper than a weaker model at high effort. You can use hill climbing to search different model effort combinations until performance drops below the baseline. Starting with a smart model at low effort is often the sweet spot for cost performance.
▶ 39:24
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
Deep research should be used for complex, multi-dimensional tasks requiring the synthesis of numerous sources, which might take a human minutes or hours. In contrast, basic web search is suited for quick, single-question queries that can be answered using a handful of sources in seconds. The recommendation is to reserve deep research for tasks demanding deep thought across many sources.
▶ 34:44
Full AI Prompting Course with Andrew Ng
Claude Tag relies on a simple, file-based approach where each channel maintains its own markdown file for memory. Instances contribute back to this shared memory during sessions to preserve context across interactions.
▶ 50:31
Claude Fable, Claude Tag, and Anthropic's Culture — Cat Wu & Thariq Shihipar ft Simon Willison
Specifications share many parallels with software code because they compose, are testable, and can be shipped as modular units. Just like traditional codebases benefit from type checkers and linters, specifications can use tooling to prevent ambiguity and ensure department consistency. This gives spec authors a robust toolchain targeted at intentions rather than syntax.
▶ 15:29
The New Code — Sean Grove, OpenAI
Switching back and forth between traditional IDE refactoring tools and agent interfaces creates unnecessary friction. Moving to a console-only workflow like Claude Code allows developers to stay directly within the project interface. It streamlines prototyping and fits well when focusing on greenfield tasks.
▶ 40:51
The Centrifuge Method: Spin the Stupid Out of AI
The OpenAI API is stateless, meaning nothing is stored on the server between requests. To maintain a conversation, you must pass the entire history of messages back in every new API call.
▶ 43:33
A Hackers' Guide to Language Models
Decoupling the brain and hands by keeping the context object independent from the harness significantly improves context engineering reliability. This architecture ensures the context is always recoverable through different tools rather than being permanently lost during compaction events. Storing state in an independent object allows long-duration agents to successfully revisit and interrogate past session logs.
▶ 57:47
Your Old Prompts Are Making Claude Worse - Lance Martin, Anthropic
To get creative ideas rather than generic common-sense responses, you should provide rich context and engage in iterative back-and-forth conversations. By giving feedback on the AI's initial options, you steer it toward more relevant and custom solutions. The recommendation is to repeat this feedback loop multiple times until you find a satisfactory idea.
▶ 45:30
Full AI Prompting Course with Andrew Ng
The US Constitution functions as a national model specification containing clear policy text that citizens can reference. It features a versioned amendment process, and judicial review acts as a grader checking compliance against the policy. Over time, these judicial precedents establish input-output pairs that serve as unit tests reinforcing the original policy.
▶ 16:48
The New Code — Sean Grove, OpenAI
New or free users often hit low request-per-minute limits initially. You can handle this programmatically by writing a retry function that catches rate limit errors, reads the retry duration, and sleeps before calling itself again.
▶ 44:53
A Hackers' Guide to Language Models
AI desktop apps can agentically gather context from your computer by finding and reading relevant files only as needed for the task. Unlike standard chat interfaces where you must manually choose and upload files in advance, these desktop applications explore folders dynamically. The best practice is to review their proposed action plans before execution to ensure safety.
▶ 1:00:08
Full AI Prompting Course with Andrew Ng
Developers should always start by writing a specification detailing expected outcomes and success criteria before writing code. They should debate the clarity of the text and make the spec executable by feeding it directly to the model. This approach embraces the reality that software engineering is about intent specification rather than writing code.
▶ 19:54
The New Code — Sean Grove, OpenAI
You pass a list of available tools or functions under the functions keyword argument in the chat completion request. Because you cannot pass raw Python functions directly, you must convert them into JSON schemas using libraries like Pydantic and Python's inspect module.
▶ 47:12
A Hackers' Guide to Language Models
Instructing models to think step-by-step is largely obsolete for modern reasoning models. Instead, you can simply tell the model to think hard or select a specialized thinking mode. This cues the model to reason extensively at length to accomplish complex tasks successfully.
▶ 1:09:38
Full AI Prompting Course with Andrew Ng
Quantized formats like GPTQ optimize models to use lower-precision data formats. This significantly reduces the memory moving around, making inference faster and allowing larger models to run efficiently on standard hardware.
▶ 1:05:30
A Hackers' Guide to Language Models
AI models possess a strong training bias to agree with users and tell them what they want to hear, which degrades answer quality. To combat this sycophancy, developers should use neutral framing and keep context factual without hinting at a preferred outcome. Presenting options as a pros and cons comparison helps elicit truly objective feedback.
▶ 1:14:30
Full AI Prompting Course with Andrew Ng
Instruction-tuned models rely on specific prefix tokens and formatting rules established during their fine-tuning process. Failing to replicate this exact prompt structure results in degraded model performance or incorrect behavior.
▶ 1:08:12
A Hackers' Guide to Language Models
Progressive outlining involves having the AI build, refine, and iterate on an outline and bullet points before generating final text. This method prevents AI slop—vague, empty-sounding text filled with overused phrases—by maintaining substance throughout the writing process. Editing an outline is high-leverage because structural changes propagate efficiently across entire sections.
▶ 1:23:26
Full AI Prompting Course with Andrew Ng
Retrieval augmented generation searches external sources like Wikipedia to find relevant documents for a given query. It then injects this retrieved text directly into the prompt context so the language model can generate an accurate, up-to-date response.
▶ 1:11:52
A Hackers' Guide to Language Models
Asking an AI to critique work without constraints often results in sycophancy, where the model gives superficial praise or arbitrary scores. Providing an objective rubric with unambiguous, binary true-or-false criteria forces the model to evaluate the work methodically. Summing scores category by category yields more accurate assessments and targeted improvement suggestions.
▶ 1:30:53
Full AI Prompting Course with Andrew Ng
You can load a domain-specific dataset using the Hugging Face datasets library and train the model using tools like Axolotl. This process modifies model weights to adapt to specialized structures, such as converting natural language questions into database SQL queries.
▶ 1:22:19
A Hackers' Guide to Language Models
Generating different data modalities varies significantly in time and computational expense. Text generation is highly efficient and low-cost, speech is moderately more expensive, images cost even more, and video generation is extremely slow and costly. These scaling differences mean that iterative prompting techniques become much harder and time-consuming when generating video or images.
▶ 1:46:00
Full AI Prompting Course with Andrew Ng
Providing image inputs enriches context, enabling AI to read handwritten notes, receipts, and brainstorm sessions. However, a major weakness is that AI views images coarsely and can struggle to distinguish fine-grained details.
▶ 1:51:23
Full AI Prompting Course with Andrew Ng
Unlike text models that generate output piece by piece, diffusion models generate an entire image all at once. They start from pure noise and sequentially learn to remove noise to reveal the final image.
▶ 1:59:29
Full AI Prompting Course with Andrew Ng
Users can prompt AI to build basic applications or games by specifying a clear goal, required inputs, and expected outputs. Simple, self-contained apps with specific tasks work best for this approach.
▶ 2:06:10
Full AI Prompting Course with Andrew Ng
When presented with structured data like spreadsheets, AI models can leverage a code execution tool to write and run computer programs. This allows them to calculate statistics, generate graphs, and extract business insights programmatically.
▶ 2:11:41
Full AI Prompting Course with Andrew Ng
No answer mentions that.