Digital Digging × AI Training for TV 2 Journalists
20 Exercises for ChatGPT
From search engine to research assistant
What ChatGPT is — and what it is not
ChatGPT is the most widely used large language model in the world. For journalists, it functions as a tireless brainstorming partner, a translator, a first-draft machine, and a surprisingly capable data wrangler. It can rewrite a press release from five different angles in seconds, extract structured data from messy text, and generate interview questions you had not considered.
But it comes with three weaknesses that every journalist must internalize before relying on it in production:
Sycophancy
ChatGPT wants to please you. If you present a wrong assumption, it will often agree rather than push back. It mirrors your framing. This is dangerous for journalists who need to be challenged, not validated.
Hallucination
ChatGPT fabricates facts, names, dates, URLs, and citations with complete confidence. It does not know what it does not know. Every factual claim must be independently verified — no exceptions.
No real source citation
When ChatGPT provides "sources," it is often reconstructing plausible-looking references from training data patterns. Even with browsing enabled, it may summarize pages inaccurately or cite URLs that do not exist. Treat every reference as unverified until you click it yourself.
These exercises are designed to make you experience these weaknesses firsthand — so you learn when to trust ChatGPT and when to verify. Every exercise includes a "What to watch for" warning. Read it before you start.
Exercises 1–6
Beginner
Get comfortable with prompting, observe how ChatGPT responds to different instructions, and start developing your editorial instinct for AI output.
The Framing Test
What you will learn
How the same facts become different stories depending on the frame. You will see how ChatGPT adapts tone, word choice, and emphasis — and how easily framing can distort reality.
What to watch for
ChatGPT may add dramatic language or imply causation in the tabloid version that was not in the original facts. It may also soften inconvenient details in the "neutral" version. Notice how the AI decides what is important — that decision is a framing choice.
TV 2 connection: David Buch uses AI daily and thinks critically about output — this exercise sharpens that critical lens on framing.
The Sycophancy Detector
What you will learn
How ChatGPT agrees with you even when you are wrong — and how it reverses position when you push back. This is the single most dangerous trait for journalists who use AI as a sounding board.
What to watch for
ChatGPT is trained on human feedback that rewards agreeable responses. This means it has a structural bias toward telling you what you want to hear. For a journalist, this is poison — you need a tool that pushes back, not one that nods along.
TV 2 connection: Lars Apel has flagged trust issues with AI tools — this exercise demonstrates exactly why that skepticism is warranted.
Quick Fact Check
What you will learn
How accurate ChatGPT is when asked about verifiable facts — especially recent Danish events. You will calibrate your own trust level with hard data.
What to watch for
ChatGPT is dramatically more accurate about well-documented historical events than recent ones. It may blend details from similar events, get dates wrong by days or weeks, or confidently state outcomes that never happened. The confidence level does not correlate with accuracy.
TV 2 connection: Peter Møller focuses on verification and scientific sources — this exercise builds a personal accuracy benchmark for ChatGPT.
Email Rewriter
What you will learn
How ChatGPT handles tone and register in professional communication — and where it over-polishes or strips essential nuance.
What to watch for
ChatGPT tends to over-formalize and add corporate pleasantries. In journalism, overly polished language can signal inauthenticity to sources. The "diplomatic" version may soften your ask so much that the source does not understand what you need.
TV 2 connection: Mathias Overgaard works with English-language research and international sources — this builds practical email drafting skills.
The Summary vs. Analysis Test
What you will learn
The difference between what ChatGPT does when you say "summarize" versus "analyze" — and why the instruction word you choose radically changes the output.
What to watch for
"Summarize" typically produces a shortened version of the text. "Analyze" often generates opinions and interpretations that may not be supported by the text. Watch for ChatGPT presenting its interpretation as if it were fact — analysis without evidence is just confident speculation.
TV 2 connection: Anne Fuglsang Borg uses AI for sorting and structuring information — understanding the difference between summary and analysis is essential for that workflow.
Headline Generator with Constraints
What you will learn
How ChatGPT handles creative constraints — and how to use it as a brainstorming partner for editorial decisions while spotting embedded framing.
What to watch for
ChatGPT frequently fails character-count constraints. It will claim headlines are under 60 characters when they are not. It also struggles with self-analysis — asking it to identify framing in its own output often produces superficial or contradictory analysis. Always count yourself.
TV 2 connection: Emil Gjerding Nielson works on editorial AI tool implementation and writing — headline generation with quality control is directly applicable to daily production.
Exercises 7–14
Intermediate
Chain prompts together, extract structured data, and start integrating ChatGPT into real journalistic workflows — always with verification.
Source Verification Chain
What you will learn
How to use ChatGPT to generate both supporting and contradicting evidence for a claim — and how to verify whether any of that evidence is real.
What to watch for
ChatGPT often fabricates plausible-sounding studies, authors, and institutions. A source might have a real organization name but a fictional report title, or a real author name attached to a paper they never wrote. The contradicting sources may be weaker than the supporting ones because ChatGPT is biased toward agreement.
TV 2 connection: Peter Møller's verification workflow directly benefits from understanding how AI-generated source chains can mislead — even when individual elements look correct.
Interview Preparation
What you will learn
How to use ChatGPT to prepare for interviews by generating questions you would think of — and more importantly, questions you would not.
What to watch for
ChatGPT may generate questions based on assumptions it made about the person — assumptions that could be wrong. Verify that the biographical details in the questions are accurate. Also watch for overly generic "unexpected" questions that sound creative but have no editorial value.
TV 2 connection: Marie Møller Munksgaard specializes in expert research and context building — AI-assisted interview prep can surface angles that manual research might miss.
Translation + Context
What you will learn
How ChatGPT handles translation beyond word-for-word conversion — and whether it can identify cultural context that would be lost on an international audience.
What to watch for
ChatGPT's Danish-to-English translation is generally good for straightforward text but struggles with idiomatic expressions, humor, and institutional nuance. It may translate "folketing" as "parliament" without explaining the proportional representation system that makes Danish politics different from, say, British politics.
TV 2 connection: Mads Buur Bach uses ChatGPT for translation and video verification — this exercise builds awareness of where machine translation needs human editorial judgment.
The Prewash Exercise
What you will learn
The "prewash" technique: making ChatGPT generate its own analysis prompt before executing it. This consistently produces deeper output than a direct instruction.
What to watch for
The prewash technique works because ChatGPT's generated prompt is often more structured and specific than what a human would write quickly. However, it may also generate an overly ambitious prompt that leads to hallucinated analysis. The key is to edit the generated prompt before executing it.
TV 2 connection: Mathias Overgaard works with large reports and error-free research — the prewash technique extracts significantly more from complex documents.
Data Extraction from Text
What you will learn
How to turn unstructured text into structured data — one of ChatGPT's strongest capabilities and most useful for journalists dealing with press releases and reports.
What to watch for
ChatGPT is excellent at extraction but may subtly rephrase claims in ways that change their meaning. A press release saying "up to 30% improvement" might become "30% improvement" in the table — dropping the crucial qualifier. Always compare extracted data against the original text.
TV 2 connection: Anne Fuglsang Borg uses AI for data sorting and structuring — this exercise directly builds the skill of turning messy text into usable data.
The Tone Analyzer
What you will learn
How to use ChatGPT to identify tone and linguistic choices across multiple sources covering the same story — a skill for comparative media analysis.
What to watch for
ChatGPT tends to label anything with strong language as "biased" and anything with passive voice as "neutral." Real editorial neutrality is more nuanced than that. It may also struggle with Danish tone — what sounds direct in Danish may be labeled "aggressive" by an English-trained model.
TV 2 connection: David Buch combines daily AI use with critical thinking — systematic tone analysis across sources strengthens editorial awareness.
The Missing Question
What you will learn
How to use ChatGPT as an editorial second pair of eyes — to find gaps in coverage that a journalist working under deadline pressure might miss.
What to watch for
ChatGPT may suggest questions that seem insightful but are actually impractical (questions the source would never answer, or questions that reveal editorial assumptions). It also tends to suggest confrontational follow-ups that sound good on paper but would damage source relationships in practice.
TV 2 connection: Lars Apel has tried AI agents and is interested in practical editorial tools — the "missing question" technique turns ChatGPT into a post-interview review partner.
Timeline Builder
What you will learn
How to use ChatGPT to build a chronological timeline from multiple sources — and where it fills in gaps with fabricated connections.
What to watch for
ChatGPT tends to create a smoother narrative than the evidence supports. It may infer causal connections between events that are only chronologically related, or fill gaps in the timeline with plausible but unverified events. A good timeline has holes — ChatGPT tries to fill them.
TV 2 connection: Pelle Lykkebo Mørk covers breaking news and has verified complex stories like E-3 Sentry damage — timeline building is essential for developing story coverage.
Exercises 15–20
Advanced
Stress-test ChatGPT's limits, combine it with manual verification methods, and build reusable workflows for your specific beat.
The Hallucination Hunt
What you will learn
How to systematically identify and document ChatGPT hallucinations — building a personal reference for how much you can trust its output on different topics.
What to watch for
When confronted with its hallucinations, ChatGPT will typically apologize and provide "corrected" information — which may also be hallucinated. This is a critical lesson: a correction from ChatGPT is not necessarily more accurate than the original error. Do not trust the retraction without independent verification either.
TV 2 connection: Christian Jessen is skeptical of AI and focused on verification — this exercise provides the hard evidence to calibrate that skepticism with data rather than instinct.
Breaking News Drill
What you will learn
How to use ChatGPT to manage information flow during a developing story — tracking what is confirmed, unconfirmed, and contradictory at each stage.
What to watch for
ChatGPT may resist categorizing claims as "contradictory" — it tends to reconcile conflicting information rather than flag the conflict. In breaking news, contradictions are the signal. Also watch for ChatGPT adding context from its training data that was not in your staged inputs — this contaminates the exercise.
TV 2 connection: Pelle Lykkebo Mørk covers breaking news and data security — a structured approach to managing information stages can reduce errors under deadline pressure.
The Comparative Framing Analysis
What you will learn
How international media frames the same event differently, and whether ChatGPT can accurately identify framing techniques across languages and editorial traditions.
What to watch for
ChatGPT may project stereotypical editorial positions onto outlets ("BBC is neutral, Le Monde is intellectual") rather than analyzing the actual text. It may also struggle with non-English articles, producing analysis that reflects its English-language training bias rather than what the original text actually says.
TV 2 connection: Mads Buur Bach works with video verification and translation — comparative framing analysis across languages and cultures is directly relevant to international story verification.
Social Media OSINT
What you will learn
The boundaries of what ChatGPT can and cannot do with public social media data — and how it compares to manual OSINT techniques.
What to watch for
ChatGPT will analyze the text content of posts but cannot see images, check metadata, analyze posting patterns over time, identify sock puppet indicators, or cross-reference with other platforms. It may also make confident inferences about someone's character or intentions that are not supported by the posts — these are editorial judgments, not facts.
TV 2 connection: Jakob Hohlmann Villumsen and Nanna both work on finding information about specific people — understanding where ChatGPT helps and where manual OSINT is irreplaceable defines the boundaries of AI-assisted investigation.
The Self-Correction Loop
What you will learn
How ChatGPT responds to iterative self-critique — and whether pushing it to correct itself actually produces more accurate output or just different errors.
What to watch for
Self-correction in ChatGPT often follows a pattern: the first correction fixes obvious errors, the second correction introduces new errors while trying to be more precise, and the third correction may abandon accurate information from earlier layers. More iterations do not equal more accuracy. Sometimes the first answer was the best one.
TV 2 connection: Christian Jessen's AI skepticism meets method here — this exercise provides empirical evidence about whether "asking again" actually helps or just generates different noise.
Build Your Own Workflow
What you will learn
How to design, test, and refine a reusable ChatGPT prompt for your specific beat — turning a general-purpose tool into a specialized assistant.
What to watch for
Prompts that work perfectly on your first test case may fail on the second. The most common failure mode is over-fitting — making the prompt so specific to one story type that it breaks on anything different. Good reusable prompts are specific about format and quality but flexible about content.
TV 2 connection: Sanne Lau Pedersen wants to build automated workflows and Emil Gjerding Nielson is implementing editorial AI tools — this exercise is the foundation for both of those goals.
When to use ChatGPT — and when not to
Use ChatGPT when you need to...
Do NOT use ChatGPT when you need to...
The golden rule: ChatGPT is a production tool, not a source. It generates text, not truth. Every output requires the same editorial scrutiny you would apply to any unverified tip. The journalist who uses AI best is the one who verifies the most.