AI automation promises to revolutionize grant writing for resource-strapped nonprofits, but missteps can damage credibility and waste precious opportunities. The key is to treat AI as a powerful assistant, not an author. Here are critical pitfalls and actionable fixes to harness AI safely and effectively.

Pitfall 1: Accepting AI Output Verbatim

Blindly using AI-generated paragraphs is a recipe for generic, off-brand proposals that fail to resonate. AI lacks your organization’s unique passion and context.

The Fix: Curate and Command Your Voice. Never accept a full section. Deconstruct the output. Use AI for structure and syntax, but you must lead with strategy and story. For example, instead of prompting “Write our project description,” use a layered approach: “I’ve described our approach; now write a compelling opening sentence for the ‘Project Description’ section.” This keeps you in the driver’s seat.

Pitfall 2: Poor Data Governance

Entering client names, specific program details, or internal strategies into public AI tools risks catastrophic data breaches and violates donor trust.

The Fix: Implement a Strict AI Data Governance Protocol. Sanitize all inputs. Before pasting any text into an AI, ask: Could this harm a client if exposed? Does it reveal non-public strategy? Does it contain names, addresses, IDs, or dates? If yes, do not use it. Use anonymized data or describe concepts without specifics.

Pitfall 3: Losing the Human Impact Hook

AI often defaults to passive, jargon-heavy language that buries the urgent human story funders need to see. It may also use acronyms without explanation.

The Fix: Edit with a Scalpel, Not a Blanket. Mandate that the first sentence of every section states a clear human impact. Rewrite AI output in active voice. Simplify jargon: prompt “Rewrite this technical paragraph for a lay audience.” Your final draft must be hopeful but urgent, speaking directly to the heart of your mission.

Pitfall 4: Unverified Facts and Figures

AI confidently generates statistics, need statements, and community data that may be outdated, incorrect, or misapplied to your specific locale. Submitting unverified claims is unethical and can disqualify you.

The Fix: Mandatory Verification Protocol. Treat every AI-generated fact as a first draft. Implement a three-step check: 1) Cross-reference with your latest internal reports. 2) Validate against trusted public sources (census data, state databases). 3) Ensure all data is current and precisely cited. If you cannot verify it, do not use it.

Pitfall 5: Disjointed Workflow

Using AI haphazardly for random tasks creates a fragmented proposal with inconsistent tone and logic, increasing editing time instead of saving it.

The Fix: Integrate AI into a Cohesive, Phased Workflow. Establish a basic AI governance checklist. Use AI sequentially: first for brainstorming outcome alternatives (“Give me five ways to phrase this literacy goal”), then for drafting outlines, then for simplifying language. This creates a structured, efficient process where AI supports, not dictates, your strategic narrative.

Ultimately, successful AI-assisted grant writing requires you to lead with strategy and story. AI assists with structure and syntax. You must verify every fact, protect every piece of data, and own the final voice. By avoiding these pitfalls, you transform AI from a risk into your most efficient writing partner.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI-Assisted Grant Writing for Nonprofits.

For coaches and consultants, AI isn’t just a tool for content generation; it’s a strategic partner for deeper client impact and business scalability. The difference between a vague request and a transformative outcome lies in the quality of your prompt. Moving from basic queries to strategic prompts unlocks AI’s true potential as a simulation engine, ideation partner, and force multiplier for your intellectual property.

The Pitfall of the Basic Query

A weak prompt like “Write a blog post about imposter syndrome” yields generic, surface-level content. It lacks direction, fails to leverage your unique expertise, and requires extensive editing. You get plausible output, not powerful, client-specific material. This wastes time and misses the opportunity to use AI for genuine innovation.

Building the Strategic Prompt: The ACES Framework

My e-book introduces the ACES framework to construct prompts that provide AI with essential scaffolding. This moves you from consumer to architect of the output.

A – Action: Start with a clear, strong verb. “Generate,” “Critique,” “Role-play,” “Summarize.” Instead of “ideas,” try “Draft three client onboarding email sequences.”

C – Context: Set the precise stage. Define your niche and client’s world. “I am a leadership coach for female tech founders in Series B companies. My client struggles with delegating to experienced hires.” This tailors the response to your specific battlefield.

E – Examples: Provide samples of your voice or past work. Paste a newsletter snippet and command, “Match this tone and complexity.” This trains the AI on your brand’s DNA, ensuring consistency.

S – Role: Assign a specific persona. “Act as an executive coach with 15 years in C-suite transition.” This directs the AI to draw from a defined knowledge base and perspective, elevating the quality of advice.

I – Intent: State the ultimate goal. “The intent is to help a new VP navigate stakeholder mapping in their first 90 days.” This aligns every generated element with your client’s desired outcome.

The Strategic Prompt Checklist

Before you hit enter, run your prompt through this checklist:

• Action-Oriented: Is the task a clear verb?
• Boundaries Set: Have I defined length, format, tone, and exclusions?
• Client-Centric: Is the context specific to my niche and ideal client’s psyche?
• Ethics Checked: Is this compliant with confidentiality and bias standards?
• Example Given: Did I provide a style sample?
• Iterative Plan: Am I ready to refine with follow-up prompts?
• Role Assigned: Did I give the AI a specific expert persona?

Transformative Applications & Tangible Benefits

This methodology turns AI into a versatile tool. It acts as a simulation tool to role-play difficult client conversations or stress-test program modules. It overcomes creative blocks by providing structured starting points for workshops or articles. Most importantly, it saves hours on research, drafting, and ideation, freeing you for high-value client work.

Perhaps the greatest advantage is how it scales your intellectual property. You can rapidly adapt a core framework into a quiz, a webinar script, or a client handout for different niches, all while maintaining your authoritative voice. You’re not starting from scratch each time; you’re intelligently repurposing your genius.

Mastering prompts is the skill that separates those who use AI for menial tasks from those who leverage it for transformative business growth. It’s about giving clear instructions to a powerful, but literal, mind.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Coaches and Consultants.

For faceless YouTube channels, the AI voiceover isn’t just a narrator—it’s your sole on-screen personality. It builds trust, conveys emotion, and retains viewers. Selecting and optimizing this voice is a non-negotiable skill for professional creators. A poor voice choice can sink engagement, while a polished one can elevate your content from generic to iconic.

Selection is Strategic, Not Random
Don’t just pick the “most natural” sample. Use a rigorous checklist. First, verify the Commercial License. Explicitly confirm the tool’s terms allow YouTube monetization; never assume. Second, test the voice’s Emotional Range. Feed it snippets from your actual scripts—can it sound curious, urgent, or somber on command? Third, audit Pronunciation Clarity. Pay special attention to niche terminology, brand names, and non-English words. A tool mispronouncing “Nicomachean” as “Nick-oh-mack-ee-an” instantly breaks credibility. The solution? Use tool-specific phonemes (e.g., `Nɪkəmˈækiən`) or spell it out phonetically in your script, and always test the output.

Optimization: The Art of SSML
Raw text is a starting point. Use Speech Synthesis Markup Language (SSML) to sculpt performance. For critical points, use “ sparingly—overuse dilutes its power. To spell out acronyms like “A-I,” use “. Most powerfully, use “ to manipulate pacing and pitch. For example, raw text: “And this brings us to the most critical factor: compound interest.” Add a “ and slow the prosody on “compound interest.” This deliberate pause builds anticipation, and the slight slowdown and pitch drop signal importance. Sync this audio cue with a matching visual: a slowed-down, serious section pairs with majestic timelapses or impactful text-on-screen. An accelerated, excited section? Use faster cuts and dynamic motion graphics.

The Actionable Routine
Integrate this final workflow. 1. Script Prep: Flag problem words; phonetically spell them. Insert SSML tags (“, “) for natural pacing. 2. Audio Polish: Run the final file through a light compressor/eq/noise reduction. 3. Final Listen: Watch the entire video without visuals. Is the audio-only narrative engaging? 4. Legal Check: Confirm all assets (voice, music, visuals) are cleared for monetization. 5. Visual Sync: Never reuse the same stock clip. Ensure your visuals are unique per video and dynamically match the voice’s cadence and emotion.

Listen for indirect feedback in comments. “Your narration is so soothing” or “I love the energy” are direct compliments on your voice optimization. Treat your AI voice as a living instrument. Select it with legal and technical precision, then compose with SSML. The result is a channel with a distinct, professional voice that captivates even without a face on screen.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI Video Creation for Faceless YouTube Channels.

AI automation is transforming e-book formatting for self-publishers, eliminating the tedious manual work once required to produce professional PDFs. The key is understanding the distinct workflows for digital and print outputs. AI tools can handle font embedding, bleed settings, and accessibility tagging with precision, but you must configure them correctly. This article outlines actionable steps to generate two essential files: an accessible digital PDF and a print-ready PDF for services like KDP or IngramSpark.

Digital PDFs: Optimizing for Direct Sales

For direct PDF sales, prioritize accessibility and small file size. Before generation, ensure your AI tool has accessibility tags enabled. During configuration, set it to embed all fonts; subsetting (embedding only used characters) is acceptable and reduces file size. Also, enable bookmarks generated from your document’s heading styles. Finally, apply a descriptive file name like BookTitle_Ebook_Accessible.pdf.

After the AI generates the file, perform a digital proof. Click every hyperlink to confirm they work. Check the document properties to verify “Tagged PDF: Yes.” Test the file size to ensure it’s optimized for download. This workflow guarantees a functional, accessible product for your readers.

Print PDFs: Meeting Industry Standards

Print production requires stricter specifications. During AI configuration, select the correct trim size (e.g., 5.5×8.5) and set the color space to CMYK (never RGB). Set a bleed of 0.125 inches or your printer’s specification. Crucially, ensure all images are at least 300 DPI. The AI should embed all fonts. Name the output file clearly, such as BookTitle_Print_5.5x8.5_CMYK.pdf.

Proofing a print PDF is non-negotiable. Always print a hard copy. Perform the “pinch test” on the printed pages to check for proper bleed and trim margins. Verify that page numbers are present and correctly placed. Inspect color saturation and image clarity. This physical check catches issues digital previews miss, preventing costly print errors.

By following these targeted AI configurations and proofing checklists, self-publishers can consistently produce professional PDFs. The automation handles the technical heavy lifting, but your oversight ensures the final product meets both digital accessibility standards and print shop requirements, saving time and maintaining quality across all distribution channels.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI-Assisted E-book Formatting for Self-Publishers.

Chaos. That’s the word for managing client revisions via endless email chains. “I prefer just emailing you quickly.” “My team member needs to see it but doesn’t have an account.” “This seems like extra work for me.” These objections stall projects and erode your professionalism. The solution isn’t just another tool; it’s a client-friendly revision portal powered by AI automation, giving clients clarity and control while freeing you from administrative overload.

The Problem with “Quick” Emails

Email-based feedback is a black hole. Comments are scattered, context is lost, and version history is impossible to track. Stakeholders reply-all with conflicting notes, and you waste hours deciphering “make the blue brighter” from ten different people. This process feels like extra work for the client because, frankly, it is. They lack a single source of truth, leading to frustration, more revisions, and project delays.

What a Client Portal Actually Does

A dedicated portal creates a structured, professional environment for feedback. But the real magic happens when you layer AI automation on top. Here’s what that achieves:

1. Visual Version Control & History: Clients see a clean timeline of every iteration, eliminating “which version is final?” confusion.

2. Contextual, Pinpoint Feedback: Instead of vague emails, clients click directly on a design element to leave a comment. AI can then automatically tag and categorize these notes.

3. Status & Approval Tracking: Clear labels like “In Review,” “Feedback Complete,” and “Approved” replace ambiguous email statuses. The AI can auto-update status based on comment resolution.

4. Consolidated Feedback Summary: This is the AI powerhouse. It clusters similar requests (“make blue brighter,” “intensify azure tone”) from all stakeholders into a single actionable item, categorizing them as “Color Adjustment.” No more manual collation.

5. Secure, Organized File Delivery: Following a professional handoff structure—a folder per client, a sub-folder per project—provides a permanent archive. The AI ensures final assets are placed correctly in this defined structure.

Implementing Your Automated Portal: A 3-Step Guide

Step 1: Tool Selection (Match to Your Stack)
Choose a portal that integrates with your existing project management and design tools (e.g., Asana, Figma, Adobe Creative Cloud). Ensure it has robust AI features for comment analysis and categorization.

Step 2: Portal Setup & Client Onboarding
Before your next project, create your consistent folder structure. Then, prepare a simple 3-step guide and a 2-minute Loom walkthrough video for clients. This onboarding is critical to overcoming the “this seems like extra work” objection by making the portal effortless for them.

Step 3: Integrating Your AI & Design Workflow (The Automation Loop)
Map your final asset delivery process. Define and communicate your exact status workflow (e.g., `Draft`, `Client Review`, `Revision Round 1`, `Approved`). Once set up, the AI takes over: it categorizes incoming feedback, clusters duplicates, and can even suggest status updates based on comment resolution, creating a seamless loop from design to approval.

This system transforms the client experience. They gain transparent control, reducing back-and-forth. You gain hours back, eliminate version confusion, and present a scalable, premium service. The initial setup is a one-time investment that pays for itself in sanity and efficiency on every subsequent project.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Freelance Graphic Designers: Automating Client Revision Tracking & Version Control.

Nonprofit grant writing is undergoing a quiet revolution. Forward-thinking organizations are moving beyond generic AI prompts to build automated, intelligent systems that drastically reduce time and increase funding success. The key isn’t just using AI, but engineering specific workflows where artificial intelligence handles repetitive analysis and drafting, freeing human experts for strategy and relationship-building. Let’s examine two concrete implementations.

Case Study: GreenRoots’ Compliance & Alignment Engine
Facing a complex RFA from an environmental foundation, GreenRoots’ team uploaded the funder’s document and their strategic plan into a single ChatGPT thread. Their custom prompt sequence instructed the AI to extract every requirement and cross-reference it with their mission. The result? In 15 minutes, they had a compliance checklist and a pre-vetted list of alignment points—a task that previously took hours of manual RFP parsing. More critically, AI flagged that their initial budget line for “miscellaneous supplies” was too vague, suggesting a more specific breakdown they immediately corrected. The generated outline was already 60% customized to GreenRoots’ language and mission, ensuring foundational compliance and alignment from the very first draft.

The Operational Workflow: From RFP to Draft
This process is repeatable and scalable. First, a consultant or grant manager uploads the new RFA/RFP into a dedicated Custom GPT (trained on past successful grants). Using a pre-vetted prompt “playbook,” they generate first drafts for standard sections like Organizational History and Capacity. The AI-generated alignment points become the proposal’s section headers. Then, using the outline, they prompt the Custom GPT section-by-section. Crucially, every draft undergoes the non-negotiable “Funder Lens” edit: “Does every paragraph answer ‘Why this? Why us? Why now?’ from the funder’s perspective?” This human-in-the-loop step ensures persuasive, funder-centric narrative.

Tool Stack & The Learning System
You don’t need a suite of expensive tools. A powerful LLM like ChatGPT (GPT-4) or Claude is sufficient, used in persistent threads to maintain context. A central knowledge base (Notion or Google Drive) feeds the AI with institutional data. For tone and clarity, tools like GrammarlyGO are useful add-ons. The magic happens in iteration. After each proposal, the team uses insights from funder feedback (wins and losses) to continually refine their Custom GPT’s instructions. This is style transfer in action—replicating a proven, funder-approved structure for a new content area. One consultant even feeds successful grants into their Custom GPT, creating a living repository of institutional winning formulas.

Beyond Drafting: Competitive Intelligence
An advanced use involves feeding the Custom GPT not just the target RFP, but also summaries of recent grants from competitor organizations in the same field. The AI then provides real-time, cited competitive landscape analysis, moving beyond generic funder profiles to answer: “How does our proposed project differentiate from what they just funded?” This allows for strategic positioning before a single word is written.

The transformation is clear: AI automates the parsing, structuring, and initial drafting; humans provide the strategic “Funder Lens,” final narrative polish, and relationship management. This hybrid model turns grant writing from a reactive, deadline-driven chore into a proactive, data-informed strategy. Organizations adopting this aren’t just writing proposals faster; they’re building institutional knowledge assets that compound with every funded project.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI-Assisted Grant Writing for Nonprofits.

Coaches and consultants know the ideal: a truly personalized plan that evolves with the client. Yet, manual tracking is reactive and fragmented. AI automation bridges this gap, creating a dynamic, responsive client experience that feels both high-touch and effortlessly continuous. The core benefit is a quantifiable, trend-based view of a client’s state without constant asking, making them feel consistently seen and understood.

The Adaptive Coaching Loop: Your New Framework

Move beyond static plans. Implement the Adaptive Coaching Loop: 1) Automated Pulse Checks gather objective data (e.g., sleep scores, journal tone). 2) AI Analysis identifies trends and flagging areas. 3) Plan Adjustment occurs automatically or via a curated prompt for you. 4) 24/7 Support Tools deliver instant, personalized resources. This cycle runs continuously, turning sporadic check-ins into a seamless growth journey.

Real-World Application: From Data to Insight

For a Health Coach: Sync wearable data (sleep, activity), analyze dietary photo logs with AI, and track daily pain/stress scores. If a client’s sleep trends down for three nights, the system can automatically suggest a recovery-focused workout modification and推送 a mindfulness resource tagged for sleep struggles.

For a Life Coach: Integrate calendar sync for time availability, use AI to detect stress or celebration keywords in journal entries, and monitor energy levels from tone analysis. When the AI notes “family conflict” keywords alongside low energy, it can prompt a relevant podcast and reschedule a low-priority task.

Implementation: Start Small, Scale Smart

Begin with one adaptive variable (e.g., weekly energy scores) and one support tool (e.g., a WhatsApp bot). Document this pilot process meticulously. Tools like Typeform (with AI analysis) or Slack/WhatsApp bots via Zapier are excellent starters. Master this for one client, then replicate and expand your “Adaptivity Profile” for others.

Follow a phased rollout:

• Week 1-2: Foundation. Audit your content library. Tag at least 10 resources with both a struggle area (e.g., #confidence) and a format (e.g., #video).
• Week 3-4: Tool Setup. Build your first automated pulse check and connect it to your tagged resource library.
• Week 5-6: Go Live & Refine. Launch with one client. After two weeks, ask for feedback on the pulse check relevance and resource suggestions. Tune your questions and tags.

Ethics & Critical Success Factors

Transparency is non-negotiable. Always disclose what data you collect and how AI uses it. Obtain explicit consent. Data privacy must be paramount; use reputable, compliant tools. The human connection remains central; AI handles the data synthesis and delivery, but you provide the wisdom, empathy, and final plan adjustments. Use prompts like: “Based on [client context] and the trend of [data point], suggest three adjustments to their confidence-building plan from our resource library,” to guide your AI-assisted review.

This isn’t about replacing you; it’s about amplifying your impact. You gain a real-time, holistic view, while clients receive hyper-personalized, immediate support. The result is a scalable, deeply individualized coaching experience that was previously impossible.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Coaches and Consultants.

For faceless YouTube channels, compelling visuals aren’t just nice—they’re everything. Your content lives or dies on the strength of your B-roll, atmospheric shots, and animations. AI automation has revolutionized this process, but success hinges on a strategic, tiered approach that blends AI generation with curated stock media for a unique, professional result.

The Three-Tier Visual Strategy

Stop relying on a single tool. Build your visuals in three distinct tiers for maximum impact and efficiency:

Tier 1: Core AI-Generated Scenes. Use Runway Gen-2 for its unparalleled control over motion and style, or Pika 1.0 for specific aesthetic vibes. Generate foundational shots like a “moving train through a mountain landscape” or “flowing data streams.” The key is a consistent prompt framework. For a tech history video, a weak prompt like “A person using an old computer” fails. Instead, use: “Cinematic wide shot, 1970s office, vintage computer terminal glowing with green text, film grain, muted color palette, slow pan—ar 16:9.” Generate 2-3 variations per essential scene using Midjourney for style or DALL-E 3 for strict prompt adherence.

Tier 2: High-Value Stock Media. Certain shots are still best captured. Use premium libraries like Artgrid or Storyblocks for time-lapses or drone footage that would be cost-prohibitive to generate. Think epic landscape moves or specific, recognizable locations like a SpaceX launch. Immediately apply your video’s color LUT (Look-Up Table) to these clips in a batch process to ensure instant visual consistency.

Tier 3: Custom Animations & Text. This is where you add branded flair. For lower thirds, kinetic typography, or abstract concept representations, use Canva for simplicity, Fliki for an all-in-one workflow, or Adobe After Effects for pro-level control. Always export animations with a transparent background (PNG sequence or MOV with alpha) for seamless layering.

Consistency is Your Brand

Your channel’s tone—whether gritty for true crime or clean/minimalist for finance—must permeate every visual. Lock down your color palettes, aspect ratios (usually 16:9), and compositional style (e.g., always centered, always rule of thirds) from day one. Use this “style sheet” to guide every AI prompt and stock selection. Atmospheric shots like “rain on a window” or “a flickering neon sign” should match this same aesthetic, creating a cohesive, immersive world for the viewer.

The 3-Day Production Workflow

Orchestrate your creation with this efficient schedule:

Day 1: Generate all Tier 1 AI visuals. Use ChatGPT or DeepSeek to brainstorm your scene list and craft your consistent, specific prompts. Aim for 2-3 variations per key scene to have options.

Day 2: Source and download all Tier 2 stock clips. Immediately apply your pre-chosen color LUT in your editor’s batch processing feature. This single step unifies disparate footage instantly.

Day 3: Build all Tier 3 animations and text graphics. Export them properly. Now, you have a complete, on-brand, and visually consistent library ready for editing.

This system avoids the clichés of generic AI art and overused stock libraries. By combining the unique power of AI with the reliability of curated stock and custom animation, you build a signature visual language that retains viewers and establishes authority. The goal is not just to fill time, but to create a captivating, professional viewing experience where every frame serves your narrative.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI Video Creation for Faceless YouTube Channels.

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For self-publishers, your e-book is a tangible extension of your author brand—a promise of a certain quality and experience. Inconsistent styling across your Kindle, print, and ePub versions doesn’t just look unprofessional; it breaks that promise and creates cognitive load, pulling readers out of your narrative. The result? Negative reviews citing that the book “looks cheap” or “differs from the print version.” Achieving pixel-perfect consistency manually across these divergent formats is a monumental, error-prone task. This is where AI-powered automation transforms the workflow from a chore into a guaranteed standard.

AI tools excel at the precise, repetitive task of style mapping. You define your master style sheet once—specifying your body text’s font family (e.g., Garamond), size (24pt), line height, and paragraph spacing—and the AI systematically applies these rules to each output format’s technical requirements. For Kindle’s limited CSS environment, it selects the closest available Kindle font (like `book-font`) and scales it to visually match your 24pt specification. For a print PDF, it embeds the exact Garamond font at 24pt with 36pt spacing after paragraphs. For ePub, it generates clean CSS3 rules like font-family: "Garamond", serif; font-size: 1.5em; using relative units (rem/em) for true reflowability.

This automation ensures your heading hierarchy remains intact. Your AI workflow maps `

` for the book title, `

` for part titles, `

` for chapter titles, and `

` for section heads, applying your branded styles consistently. On Kindle, this might mean leveraging the device’s native heading styles within its constraints. In the print PDF, it means absolute positioning with exact margin and bleed control. The AI handles the metadata mapping, translating your design decisions into the correct HTML tags and CSS classes for every format, eliminating manual guesswork.

Consistency must extend to every special element. Your blockquote style—whether italicized, indented, or with a border—must be identical. Figure captions, typically in a smaller, centered, italic font, cannot vary. Technical elements like code blocks require a consistent monospace font and background shading. Footnotes need standardized superscript numbering, sizing, and separator lines. AI ensures your specifications for these elements (e.g., “Print/ePub: Font: EB Garamond Bold, 24pt, Color #2A5CAA”) are applied universally, whether in a CMYK print PDF or a fully accessible ePub with semantic tagging.

The ultimate goal is a seamless, unified reader experience. A reader starting your book on a Kindle should encounter the same visual rhythm and branding as one reading the print edition or an ePub on a tablet. This consistency reduces cognitive friction, reinforcing your professional brand and

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI-Assisted E-book Formatting for Self-Publishers.