Summary of "Narzędziownik AI 2.0 Reloaded - sesja 1"

Overview

Session 1 of “Narzędziownik AI 2.0 Reloaded” (Toolmaker AI series, Tomek Turba) is an introductory, practical training. It covers:

Core AI concepts and popular models
Prompt engineering techniques and demos
Security, ethics, and risk management
Course logistics (14 sessions total)

Emphasis is hands-on: choosing models/tools, crafting prompts, reducing hallucinations, deploying safe RAG/local setups, and integrating AI into business workflows.

Logistics / Product of the training series

14 scheduled sessions (recordings + slides emailed after each meeting)
Certificate + test (Polish & English) with CPE points
Minibook listing ~120 tools
Community/communication: Sekurak.pl resources, Securitum company trainings, Discord channel (Sekurak), mailing list
Future demos & labs: local model setups, building assistants, agents, automation, multimedia creation, Microsoft Copilot, SEO/AEO/GEO positioning

Key technological concepts explained

Model families and architectures: Transformer, LLMs (large language models), LRMs (deep reasoning/long-context models)
Core mechanisms: embeddings, attention, tokenization, context window/tokens
RAG (Retrieval‑Augmented Generation) — local/document RAG for safer corporate answers
Embedding databases / vector DBs (Qdrant mentioned) for document search/RAG
Hardware & scaling: GPU (NVIDIA) vs CPU; clusters/supercomputers required to train very large models
Local vs cloud models: tradeoffs between privacy/safety and knowledge/freshness/compute
Model marketplaces and datasets: Hugging Face and model/dataset discovery

Models, platforms and notable products mentioned

Cloud / general models:

OpenAI (ChatGPT / GPT‑5.2 mention)
Google Gemini (Gemini Pro / deep reasoning mode)
Microsoft Copilot / GitHub Copilot
Anthropic Claude
Meta LLaMA
Mistral
X (Grok)

Specialty / deep‑reasoning:

Deepsig R1 (deep reasoning model) — local download recommended vs using a questionable frontend
Perplexity (aggregator + deep reasoning/search), Perplexity Pro

Local / OSS / Polish:

LLaMA variants
Belik (Polish model)
GGUF format for CPU runs

Tools & services demoed or referenced:

Gemini image editor (image-to-image & avatar edits)
Perplexity, Hugging Face, platform.openai.com (sandbox)
Manus AI (agent/builder)
Fireflies (transcription/meeting capture)
Leonardo / “Nano Banana Pro” (image generation model label)
tataf.com (tool directory)

Prompt engineering — structure and techniques

Four core components of a good prompt:

Instruction (verb), context, data input, answer format/constraints

Seventeen prompting techniques covered:

Zero‑shot prompting
One‑shot prompting
Few‑shot prompting
Instruction‑based prompting
Role/persona prompting
Chain‑of‑thought prompting
Step‑by‑step prompting (explicit)
ReAct (reason + action)
Tree‑of‑thoughts
Self‑consistency (generate multiple solutions then pick)
Prompt chaining (series of smaller prompts)
Metaprompting (prompt to create/optimize prompts)
Contextual prompting (provide rich document/code)
Structured/output format constraints (JSON/XML/table)
Negative prompting (specify what to avoid)
Prompt injection awareness / jailbreaking mitigation
Multi‑agent / planner‑executor prompting

Practical tips:

Prefer structured outputs (JSON) for integrations
Use delimiters for context and to reduce injection risk
Combine techniques (e.g., few‑shot + role + format constraints)
Prefer English for multimodal/generative prompts (most models trained predominantly on English)
Use negative prompts to avoid artifacts
Use metaprompts to iterate and improve prompt quality

Product demos and practical examples

Photo editing/generation: uploaded Tomek’s photo to Gemini and generated full‑body knight armor + lasers — highlighted speed vs manual Photoshop
Valuation & sales advice: uploaded photo of an Amiga 500 set; model recognized components and produced pricing, market tactics, and sales copy
Travel planning: complex Peru trip prompt using deep reasoning mode for updated itineraries and safety tips
Sandbox usage: platform.openai.com for testing system/developer messages, comparing models, and optimizing token costs

Security, ethics, regulation, and risk management

Hallucinations: what they are and mitigations (deep reasoning, verification, delimiters, human‑in‑the‑loop)
Prompt injection and jailbreaking: demonstrated as real risks; importance of AI cybersecurity measures
Data leaks: avoid personal/free accounts for corporate confidential data — use business licenses or local models for sensitive data
Explainability & bias: risks such as hiring discrimination; importance of Explainable AI and transparency
EU AI Act / Code of Good Practice: model cards, transparency requirements, copyright/licensing implications, model risk classification (forbidden/high/medium/low), and regulatory challenges
Copyright & authorship: AI‑generated works require significant human input to qualify as protected authorship; check model/tool licenses

Practical recommendations & best practices

Use business accounts/licenses for company work
For sensitive corporate content prefer local models + RAG with vetted embeddings
Verify AI outputs with independent sources before acting (medical/legal/critical decisions)
Build human‑in‑the‑loop controls; avoid full autonomy for critical transactions
Optimize token usage for cost control; test models in sandboxes before production
Combine multiple models/platforms where appropriate (aggregators like Perplexity can be cost‑effective)
Develop AI competencies: data literacy, low‑code/no‑code skills, prompt engineering, critical thinking, basic model ops

Course / Tutorial — future sessions (high level)

Intro to AI, models, prompting (this session)
Multimedia AI: image, audio, video, 3D, local creative tools (create anthem)
Business tools: project management, BI with AI
Microsoft Copilot (Chat, 365, Studio) + risks
AI for parents & children (education)
Cybersecurity, leaks, deepfakes, attacks
Building personal assistants, persona/prompt management
Designing AI-enabled applications, low/no‑code builders
Safe company implementation (local models, configuration)
AI agents & automation (multi‑agent setups)
Sales & social media automation with AI
Research & education use cases
Deep technical anatomy of models (algorithms, layers)
SEO / AEO / GEO positioning in the AI era (bonus / date tbd)

Additional resources: OSINT toolkit course, minibook of tools, Sekurak AI columns, possible workshops on model tuning and local deployments.

Mentions of reviews / comparisons / recommendations

Perplexity Pro: recommended as a cost‑effective aggregator
Gemini: rated subjectively strong for images and multimodal tasks
Deepsig R1: flagged as a major LRM with good deep reasoning; security concerns about data routing
Claude (Anthropic): noted for large context windows and suitability for programming tasks (Cloud Code)
Grok (X): described as a “dark horse” with fewer safety constraints but ethical considerations
Advice: evaluate models per task, test in sandbox, prefer local installs where confidentiality matters

Main speakers / sources

Main presenter: Tomek (Tomasz) Turba — cybersecurity & AI trainer, Securitum / Sekurak.pl
Organizations / platforms referenced: Securitum, Sekurak.pl, Hugging Face, OpenAI, Google, Anthropic, Deepsig (DeepSeek R1), Perplexity, Meta (LLaMA), Manus AI, Qdrant, tataf.com
Contributors referenced: Łukasz Łopuszański (colleague, AI columns concept), guest contributors planned (e.g., Michał Sajdak for deepfake session)

Extras (available outputs)

Short checklist for secure corporate AI adoption (extractable)
Concise prompt engineering cheat‑sheet (templates for zero-/few‑shot, role prompts, JSON output, negative prompts)