How Can xAI Assistants Craft News Content to Match Unique Writing Styles?

In an era of information being abundantly available and rapidly consumed, the high-quality, tailored news content has never been more in demand. With advancements in explainable AI (xAI), the ability of AI assistants to create news articles that align with unique writing styles is transforming the media landscape. This post will explore how xAI assistants achieve this, the benefits they offer, and the implications for journalists and content creators.

Understanding xAI and Its Capabilities

xAI refers to machine learning models that provide insights into their decision-making processes. This transparency is crucial in applications like news writing, where understanding the rationale behind content creation can bolster trust and credibility.

AI assistants equipped with xAI capabilities can analyze an individual's writing style, tone, and preferred vocabulary. By dissecting these elements, they can replicate the intricate nuances of a specific writer's voice, while adhering to the essential principles of journalistic integrity.

The LLM proprietary model developed by xAI is currently referred to as Grok 4. This is trained on a Colossus supercomputer cluster. The reinforcement learning training phase has about 10 times more compute power than previous models and 6 times overall efficiency improvement. As of mid-2025, Colossus reached approximately 230,000 NVIDIA GPUs, from which there were 30,000 GB200s. Later, it added 550,000 GB200 and GB300. The roadmap for future models targets 1 million GPUs by late 2025.

Analyzing Writing Styles and Synthesizing Information

xAI assistants rely on a sequence of activities for adaptation to various stylistic requests.

Fine-tuned embeddings

The starting point is building pre-trained general embeddings that encode general language patterns from a variety of input texts. Embeddings are numeric representations of semantic and syntactic properties of the linguistic units in a high-dimensional vectorial space.

Stylistic Parameter Adjustment

The weights of its neural network are then adjusted during a fine-tuning, specialized training which feeds relevant datasets that emphasize certain styles, tones, or voices. The stylistic parameter adjustment emphasizes certain linguistic traits such as formality, emotional or cultural nuances. During the output refinement, multiple generated responses are iteratively evaluated against desired styles, tone and voice.

Pattern Recognition and Contextual Analysis

Let's explore the building blocks behind the alignment of the fine-tune embeddings to the input provided by the end-user during a prompt session. The text and possible instructions are analyzed for linguistic patterns such as the sentence structure, vocabulary and rhetorical devices.

Sentence structure

The grammatical arrangement of words in a sentence determines its meaning. The sentence based hierarchy is follows:

Basic elements	Subject, Verb, Object, Complement, Adjunct.
Sentence Types	Simple: one independent clause. Compound: at least two connected independent clauses. Complex: one independent clause and at least one dependent clause. Compound-Complex: two independent clauses and one dependent clause.
Common Sentence Patterns	Subject-Verb (SV). Subject-Verb-Object (SVO). Subject-Verb-Complement (SVC). Subject-Verb-Indirect Object-Direct Object (SVIODO).

Vocabulary

The body of words used on a particular situation is characterized by the following:

Lexical words	Nouns, verbs, adjectives, and adverbs.
Grammatical words	Articles, pronouns, conjunctions, and prepositions.
Types of vocabulary	Listening, speaking, reading, and writing vocabularies.
Word families	Groups of related words (e.g., "happy," "happiness," "happily").
Synonyms and antonyms	Words with similar or opposite meanings.
Connotations	The emotional associations of a word,

Rhetorical devices

The techniques used in language to create a specific effect on the audience are called rhetorical devices. For example, a word that imitates, suggests, or resembles a source of sound is an onomatopoeia. The repetition of a sequence of words at the beginning of neighboring clauses, to support the emphasis, is an anaphora. From purpose and effect perspective, common rhetorical devices are for emphasis, structure, imagery and sound, persuasion, and irony/humor, The following is a representative but not exhaustive list: alliteration, anacoluthon, anadiplosis, analepsis, anaphora, antanaclasis, antiphrasis, antonomasia, apophasis, aporia, cacophony, chiasmus, dialogism, dysphemism, epistrophe, epizeuxis, hypallage, hyperbaton, hyperbole, hypophora, litotes, meiosis, metaphor, metonymy, onomatopoeia, oxymoron, pleonasm, simile, syllepsis, synecdoche, zeugma.

Prompt-Driven Adaptation

When the assistants are asked to mimic a specific style, the given input is used to further align the fine-tune embeddings to words and phrases with stylistic markers such as formal vocabulary, rhythmic sentence structure, or emotional undertones. Therefore, the relevant stylistic features are prioritized. The appropriate cultural references, idioms, and historical context receive maximum weight when matching a specific period-appropriate language and worldview.

Transformer Architecture and Synthesizing Information

During contextual adaptation, the fine-tuned embeddings are used to map the prompt input to the desired output with the help of transformers. These are normally implemented as an encoder-decoder. Due to the fact that xAI is optimized on language generation, Grok makes only use of a dense, decoder variant. The essential parts of an xAI transformer are:

• Input Embeddings: text is tokenized and converted intro numerical vectors (embeddings). Positional encodings are added to indicate word order.

• Self-Attention: each word in the input attends to every other word so that amount of focus is calculated based on relevance. The process is referred as scaled dot-product attention. In order to capture syntactic and semantic relationships, multiple attention operations are ran in parallel.

• Feed-Forward Neural Networks: the non-linear, complex patterns are learned by passing each word representation through a position-wise feedforward network. The training includes diverse sources, such as classical rhetoric, linguistic texts, and real-world examples with balanced viewpoints, which mitigate bias.

• Layer Normalization and Residual Connections: the self-attention and feedforward NN are placed in one layer. While early layers handle basic facts, the deeper ones synthesize nuances. In this way, they are stacked, to build abstraction. To prevent vanishing gradients during backpropagation, each layer includes residual connections that add the input to the output. The normalization ensures consistent scale across all features.

• Stacked Layers: multiple identical layers in the decoder enable the model to learn increasingly abstract patters, by having each layer refining the representation.

• Output Layer: the final layer is a probability distribution, created iteratively over the vocabulary for the next token, generated using a softmax function. The type of synthesis capability achieved by the previous steps respects causality. The autoregressively generated token sequence is detokenized into readable text. The previous training on formatted text is eventually used in post-processing engagement, such as markdown for list and tables. Connections are further synthesized, by incorporating the conversation history.

  
  

The advantage of the transformers over the recurrent neural networks is that they scale better by parallel processing of tokens, therefore speeding up the training and inference. The understanding of complex sentences is achieved by long range focus on words, which enables much longer or global contexts. This is also the advantage of transformers over

convolutional neural networks which are more suitable for detecting local patterns (edges and textures), by applying filters via sliding windows, inside convolutional layers, to grid-like data (e.g. images).

To summarize, the synthesis is a layered, attention-driven fusion of input, embedded knowledge graphs (learned during training), and contextual patterns, producing specific informative responses. The process mirrors the operation of connecting rhetorical devices back to transformer mechanics, during a conversation session.

Benefits of Using xAI Assistants

In general, xAI assistants exhibit advanced reasoning and problem-solving complex tasks, truth-seeking and balanced perspectives, adaptability of style, tone, and voice, accessibility and multi-platform availability.

The integration of xAI assistants in news content creation offers several compelling advantages:

1. Consistency

Organizations can ensure that articles adhere to a strict style guide, maintaining a unified voice across their publications. This consistency fosters brand identity and reader loyalty.

2. Efficiency

AI technology can significantly reduce the time it takes to produce articles.  Large queries are handled efficiently. The information is synthesized across conversation history. By automating the research and drafting phases, xAI assistants free up journalists to focus on more complex tasks, such as investigative reporting, brainstorming, and developing deeper insights.

3. Personalization

Understanding individual reader preferences and generating content that aligns with them can enhance engagement. xAI assistants can tailor news articles not just to suit a writer's style but also to resonate with targeted audience segments, ensuring greater relevance and reader satisfaction.

4. Scalability

xAI assistants can produce a large volume of content without sacrificing quality. This scalability means that news agencies can cover more stories and respond quickly to breaking news, thereby staying ahead of competitors.

Challenges and Considerations

While the benefits of xAI assistants are significant, there are challenges to consider.

Ethical Concerns

The use of AI in news writing raises questions about authorship and ethics. If an AI creates content that imitates a human writer's style, who holds ownership rights over that content? Additionally, there is the potential for misinformation if xAI models are not trained correctly or if they rely on biased datasets. As we move forward, it will be essential for journalists, content creators, and media organizations to embrace these advancements responsibly, ensuring that quality journalism remains at the forefront of the news landscape.

Dependence on Technology

Relying heavily on AI for content creation may diminish the unique human touch that traditional journalism provides. Readers may yearn for authentic storytelling that connects with them on a personal level, something that AI may struggle to replicate fully.

Quality Control

Although xAI can generate articles quickly, human oversight is essential to ensure factual accuracy and adherence to ethical journalism standards. Ideally, AI should function as a supportive tool rather than a replacement for human writers.

Real life application

Use case

In the previous post, we considered the case of a journalist who requested reference material for Salem Witch Trials, Electromagnetism, and J C Maxwell Biography, in a prompt session. In response, the xAI agent mapped the requests to specially tailored functions that gathered and parsed the appropriate content. We are extending the work scope: the journalist intends to write articles about the topics of Wildfires in California, Poverty Around the World in 2024 and Climate Change, using the style expressed in the three reference materials.

The journalist's audience consists of a range of fragmented population cohorts who share one or multiple similar features such as demographics, exposure to specific event, professional roles, health history, interaction and dialog style, interests, preferences for personalized content, etc. With digital media consumption on the rise, managing voluntary churn is critical for news organizations that rely on subscription revenue for long term sustainability. Therefore, a journalist should be highly motivated in employing a combination of writing styles that can optimally target the audience.

The style mixture in the retrieved reference material (word count included in parentheses) can be describes as follows:

• Salem Witch Trials (1,590 words): expository, historical, and scholarly style with a clear, structured, and accessible approach. It combines factual reporting, expert analysis, and narrative elements to provide a comprehensive understanding of the Salem witch trials, appealing to readers interested in history and cultural studies.

• Electromagnetism (1,284 words): scientific, technical, and expository style characterized by precise terminology, mathematical rigor, and a formal, scholarly tone. It combines theoretical explanations with historical context and practical examples, structured logically to educate readers about Maxwell’s equations and their impact on electromagnetism. The style is tailored to an audience with a foundational understanding of physics, aiming to deepen their knowledge through clear, authoritative, and analytical prose.

• J C Maxwell Biography (899 words): biographical, historical, and scientific style that combines a chronological narrative of Maxwell’s life with clear, expository explanations of his contributions to physics. Its formal, scholarly tone, balanced with accessible language and personal anecdotes, makes it suitable for readers interested in the history of science, biography, or the development of electromagnetic theory. The celebratory tone and interdisciplinary focus further enhance its appeal, portraying Maxwell as a multifaceted genius whose work reshaped modern physics.

The journalist will prompt the xAI agent to generate articles on the three topics of interest, using styles from the three reference materials.

Solution

The implementation is in Python 3.8+. OpenAI API library provides convenient access to the xAI REST API.

Building blocks

In design, we will reuse the SourceAgent class, derived from XAIAgent, both described in the previous post. This serves in retrieving the reference material, by invoking the Retrieve method of three classes which were also described there: SalemWitchTrialsSource, ElectromagnetismSource, and JCMaxwellBiographySource, respectively:

Authoritative sources class inheritance

We need an extra class derived from the XAIAgent, to generate response for a given topic, using a specific source. Let's calls that QuoteAgent.

Agent class inheritance

The constructor of the class QuoteAgent takes an instance of the agent, a system message and a prompt.

The system message instructs the agent to mimic a given style and maintain it in all responses. Example:

"Analyze this writing example and mimic its style, tone, and voice in your responses:

[inserted reference text].

Maintain this same writing style in all your responses."

In the case of the Salem Witch Trials reference, the inserted reference looks like below:

The prompt asks the agent to write a post about a given topic. Example:

Write a post about this news: Wildfire in California.

The generate_response method of class QuoteAgent invokes the agent client with the message and the prompt, once per minute, until successful completion. The trial loop avoids exceptions resulted from Grok maximum resource capacity utilization.

Example of responses for Wildfires in California topic, generated with

Salem Witch Trials style:

Electromagnetism style:

J C Maxwell Biography style:

Main logic

To visually represent the whole process, let's look at following sequence diagram:

Source Agent Experiment Sequence Diagram

As mentioned earlier, the execution of the program starts with retrieving the reference material for the three sources:

For each type of source, one individual message is prepared:

Similarly, a single prompt is prepared for each topic of interest:

A Cartesian product of size 9 is build with all messages and prompts. The responses are generated and aggregated for each element of the product, in 40 iterations, below:

Finally, for each pair message-prompt and it's 40 distinct responses, a JSON file is created:

Considering the waiting time of one minute between iterations and about 20 retries resulted from maximum cluster capacity utilization exceptions, the total execution time was 380 minutes, at a cost of less than 1.80 USD.

For more details, check out the source_agent_experiment branch of the repository x.ai.function-calling.git. The execution program is located in QuoteAgentExperiment.py file.

Conclusion

The synergy between xAI assistants and news content creation represents a paradigm shift in journalism. As xAI technology continues to advance, its capabilities in crafting news content will likely evolve further. The integration of natural language processing and machine learning algorithms will facilitate even more seamless interactions between AI and human writers. This evolution in news content creation is not just about technology; it is fundamentally about how we communicate, connect, and inform in an ever-changing world.

Moreover, the potential for real-time analytics means that content can be adjusted on the fly based on reader engagement metrics. This agility presents exciting possibilities for news agencies looking to enhance their digital offerings.