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Spotlight
PAGE: https://alison.com/course/prompt-engineering-with-generative-ai
PROMISE: Reduce inconsistent AI outputs by mastering structured, reliable prompt design.
WHO: Small business owners and marketing teams struggling with inconsistent generative AI outputs.
WHYNOW: Generative AI adoption is accelerating, making reliable prompt skills essential for competitive output.
DESC: Learn Prompt Engineering to design effective inputs for Generative AI, LLMs, and chatbots. Master advanced prompting skills with real-world examples.
BEFORE: Creative sessions stall when AI outputs drift off target.
BEFORE: Repetitive mistakes require manual editing every single time.
BEFORE: There is no consistent method for checking if a prompt works.
AFTER: You design inputs that keep AI focused on the intended creative path.
AFTER: Validation checks catch repetitive errors during the drafting phase.
AFTER: You use A/B testing to confirm prompt reliability before final use.
GAIN: Concrete skills for controlling temperature and context window limits.
GAIN: Methods for reducing ambiguity through token budgeting and validation.
GAIN: A structured process for evaluating prompt performance and iterations.
STEP: You write prompts that yield different results each time without clear logic. || Explain the way tokens, context windows, temperature, and top-p affect language model outputs and behavior || You adjust generation settings deliberately instead of guessing why responses vary.
STEP: Instructions are too vague, causing the model to miss key requirements repeatedly. || Assess ambiguity, token budgeting, and validation checks to improve prompt reliability and accuracy || You build in specific checks and tighten wording to catch errors before they happen.
STEP: You cannot prove whether a new prompt structure actually works better. || Evaluate prompt performance through A/B testing, cost optimization, and human feedback iterations || You test prompt variants against real criteria to confirm improvements before scaling.
NOTFOR: Developers looking for backend code or API integration tutorials.
NOTFOR: Users who need immediate, zero-friction auto-prompting tools without learning.
FACT: Average duration: 3-4 Hours
FACT: CPD-accredited certificate available
FACT: Intermediate level
COVERS: Define the core concepts, scope, and terminology involved in prompt engineering and generative AI models
COVERS: Explain the way tokens, context windows, temperature, and top-p affect language model outputs and behavior
COVERS: Identify key use-cases and limitations of prompt engineering across various industries and domains
COVERS: Distinguish between system prompts, user prompts, and techniques for setting clear instructions effectively
COVERS: Generate prompts with tailored tone, style, and persona alignment to suit diverse audience requirements
COVERS: Assess ambiguity, token budgeting, and validation checks to improve prompt reliability and accuracy
COVERS: Apply chain-of-thought strategies to design stepwise prompts that encourage transparent reasoning paths
COVERS: Compare methods for dialogue state tracking, slot filling, and session summarization in multi-turn designs
COVERS: Create programmatic prompts using template variables, conditional logic, and automation tools efficiently
COVERS: Evaluate prompt performance through A/B testing, cost optimization, and human feedback iterations
COVERS: Analyze applications of prompt engineering in content generation, data tasks, and specialized domains
COVERS: Discuss ethical concerns including bias, privacy, copyright, and hallucination management in AI systems
MODULE: Foundations of Prompt Engineering || Generative AI Essentials | Prompt Building Blocks | Tone & Style Control | Prompt Debugging Basics
MODULE: Advanced Techniques and Optimization || Chain-of-Thought Design | Multi-Turn Conversation Design | Programmatic Prompting | Performance Evaluation
MODULE: Applications, Ethics and Future Trends || Content Generation Use-Cases | Data and Knowledge Tasks | Specialized Domain Prompts | Ethics & Safety
What you are probably seeing
Your batch runs produce a mix of valid and broken assets, with the same tool call errors appearing every time you start a new session.
You notice the model loses track of instructions or spatial details it had correctly handled just a few steps earlier in the process.
Why it happens
Modern language models have limited memory windows and struggle to maintain precise context over long creative sessions, causing them to forget earlier constraints.
Automated agent loops often fail to log specific failure patterns, so the system keeps repeating the same logical mistakes without learning from previous errors.
What changed recently
2026-10-07 · Apple is locking down AI agents. Your WhatsApp AI needs the same rules.
Apple is now restricting AI agent scopes on Mac:
• Full Disk Access tightened for AI agents.
• AI off by default, enabled per connection.
• Restricted context never sent to the model.
Source: how_devto_ai
2026-10-07 · Changing what my phone agent was shown beat changing the model
DEV Community case studies show why AI agents fail at context maintenance:
• Changing agent notes cut invented emails from 17 of 216 runs to zero.
• Omitting a fact causes the model to invent it instead of looking it up.
Source: how_devto_ai
2026-10-06 · How AI decision models could change content moderation
Musubi released PolicyLM-1.7B to fix this gap:
• Applies plain English policies in under 50 milliseconds
• Outputs binary judgements rather than text
• No new training required when content policies change
Source: techcrunch_ai
2026-10-06 · An AI agent made a tiny drug R&D mistake. Then it kept going.
PharmaVoice covers new risks for AI error handling:
• ChemAgent failed to fix invalid input.
• 37,000 AI agents annotated 55,984 trials.
Source: PharmaVoice
2026-10-06 · Nano Banana 2.1 vs Nano Banana 2 vs Nano Banana Pro: Hangisini Kullanmalısınız?
Google released Nano Banana 2.1 on Oct 6 to cut costs by 50%:
• 1K output price is $0.0336 per image
• Supports 1K, 2K and 4K resolutions
• Thinking tokens cost $7.50 per 1M
Source: how_devto_ai
2026-10-06 · Claude Sonnet 5.5 Migration: Fix API Errors & Tool Calls
Kingy AI is now migrating guides for Claude Sonnet 5.5 to fix API errors:
• Use claude-sonnet-5-5 as the model name without a date suffix.
• Reject tool_choice types any and tool, using auto instead.
Source: Kingy AI
2026-10-06 · Mistral unveils new AI model it says rivals best open systems from China
Mistral is now offering a new tool for this gap:
• Mistral Large 4 is a 1-trillion-parameter open-weight model.
• Claims to be the strongest open-weight model developed outside China.
• Currently in preview for developers and cybersecurity leaders.
Source: CNBC
2026-10-06 · Microsoft Foundry Crunches Volumes of Agent Activity To Investigate, Correct Errors
Microsoft Foundry now analyzes agent traces for that gap:
• Finds error patterns in stored traces.
• Surfaces recurring behaviors and likely causes.
• Identifies failures in tool calls and context issues.
• No updates happen without human review.
Source: Cloud Wars
Step by step
1. Export your recent agent interaction logs and open them in a text editor to manually identify where the context drift begins.
2. Check for repeated error codes in the trace files to spot which specific tool calls are consistently failing.
3. Review the sequence of instructions to find where the model likely lost track of the original creative direction.
4. Create a checklist of the exact constraints that were forgotten and add them to the prompt structure for the next run.
5. Schedule a weekly review of your failure logs to adjust your workflow rules and prevent the same repetitive issues from returning.
Ways to go further
Start here: Prompt Engineering With Generative AI Open the free course on Alison ↗
Affiliate links: CHEAPUNO may earn a commission if you buy through them, at no extra cost to you.
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