Use Custom Prompts to Generate Domain-Specific Video Summaries

Generic AI summaries treat every video the same way. A quarterly earnings call, a medical lecture on pharmacokinetics, and a software architecture talk all get flattened into the same broad bullet points. For teams that depend on precise domain terminology, this one-size-fits-all output creates more work than it saves. The

custom_instructions

Why do generic summaries fail for specialized content?

General-purpose summarization models optimize for readability and coverage across all possible topics. That design goal directly conflicts with the needs of domain specialists who require precise terminology, structured metric extraction, and context-specific framing. When a financial analyst processes an earnings call video, they need GAAP versus non-GAAP revenue figures, margin percentages, and forward guidance language. A generic summary might mention "the company discussed revenue growth" without capturing the specific numbers that make the summary actionable.

The Stanford HAI Annual AI Index Report found that domain-specific language models outperform general-purpose models by 15-25% on specialized extraction tasks. That performance gap translates directly into missed data points, mischaracterized terminology, and summaries that require manual correction before they can be used in professional workflows. When 73% of knowledge workers already spend more time searching for information than creating it (McKinsey, 2023), adding a manual correction step to every AI-generated summary defeats the purpose of automation.

The root issue is that general models lack the implicit context that domain experts carry. A cardiologist reading a transcript knows that "EF" means ejection fraction, not an abbreviation for something else. A general summarizer might skip the term entirely or expand it incorrectly. Custom instructions bridge this gap by providing the domain context that the model needs to produce specialist-grade output.

How does the custom instructions parameter modify AI output?

The

custom_instructions

tldr

detailed

study_notes

timestamped

key_insights

Think of custom instructions as a briefing document for an analyst. Rather than handing someone a transcript and saying "summarize this," you hand them the transcript along with specific guidance: "Extract all revenue figures, compare them to the prior quarter, and flag any forward-looking statements." The AI follows these instructions in the same way, weighting the specified extraction targets more heavily than general content.

Custom instructions do not replace the summary mode. Instead, they layer on top of it. If you select

key_insights

Here is a curl example that submits a video with financial extraction instructions:

curl -X POST "https://api.yt2text.cc/api/v1/videos/process" \
  -H "X-API-Key: sk_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "video_url": "https://www.youtube.com/watch?v=VIDEO_ID",
    "summary_mode": "key_insights",
    "custom_instructions": "Extract all financial metrics including revenue, EBITDA, net income, and margins. Compare stated figures to prior quarter or year when mentioned. Flag forward-looking statements and management guidance separately. Use exact numbers from the transcript, not rounded approximations."
  }'

The response returns a

job_id

GET https://api.yt2text.cc/api/v1/videos/status/{job_id}

GET https://api.yt2text.cc/api/v1/videos/result/{job_id}

What prompt patterns produce the best results for financial content?

Financial content has the most structured extraction requirements of any domain, which makes it an ideal starting point for custom prompt design. Effective financial prompts share three characteristics: they specify the exact metrics to extract, they define how comparisons should be framed, and they separate factual reporting from forward-looking language.

A strong financial prompt includes an explicit list of target metrics. Rather than "extract financial information," specify "extract revenue, gross margin, operating income, EPS, and free cash flow." This specificity prevents the model from choosing which metrics seem most important and instead ensures consistent coverage across every video you process.

Comparison framing matters because financial figures without context are rarely actionable. Add instructions like "when year-over-year or quarter-over-quarter comparisons are stated, include both the current and prior period figures." This produces output like "Q3 revenue was $4.2B, up 12% from $3.75B in Q3 of the prior year" rather than simply "revenue increased."

Separating forward guidance from reported results is critical for compliance. Instruct the model to "clearly label any management projections, guidance ranges, or forward-looking statements with a FORWARD-LOOKING prefix." This makes downstream compliance review faster and reduces the risk of treating projections as confirmed results.

How do you craft prompts for medical, legal, and technical domains?

Each domain has its own extraction priorities and terminology conventions. The following table shows example custom instructions for four common domains, illustrating how the same structural approach adapts to different content types.

For medical content, precision in terminology is non-negotiable. Instruct the model to "use the full generic drug name followed by the brand name in parentheses on first mention" to prevent ambiguity. For legal content, citation format matters: specify "use Bluebook citation format for case references" if your team follows that convention. For technical content, version specificity is the priority: "Python 3.12" carries different implications than "Python 3" and the prompt should enforce that distinction.

The common thread across all domains is explicitness. The more specific your instructions, the less the model has to guess about your intent. Vague instructions like "focus on important details" leave the definition of "important" to the model. Specific instructions like "extract all API endpoint URLs, HTTP methods, and authentication requirements" leave no room for interpretation.

How do you test and iterate on custom prompt effectiveness?

Treat custom prompts as code that requires testing and version control. Process the same video with different prompt variations and compare the outputs against a manual extraction that serves as your ground truth. This A/B approach reveals which prompt phrasings produce the most complete and accurate results for your domain.

Start with a baseline: process a well-understood video using a summary mode without custom instructions. Then process the same video with your draft custom instructions. Compare the two outputs against your ground truth on three dimensions: completeness (did it capture all target data points), accuracy (are the extracted values correct), and format compliance (does the output follow the structural conventions you specified).

Track prompt versions alongside their performance scores. When you find a prompt that reliably produces 90% or higher completeness for your domain, lock it as your production template. Continue testing with new videos periodically to ensure the prompt generalizes beyond your initial test set. If performance degrades on certain video types, such as panel discussions versus single-speaker presentations, consider maintaining separate prompt templates for each format.

For teams managing prompt libraries across multiple domains, the Python example below demonstrates a pattern for maintaining and selecting domain-specific templates programmatically:

import requests

DOMAIN_PROMPTS = {
    "financial": (
        "Extract all financial metrics: revenue, EBITDA, net income, EPS, "
        "and free cash flow. Include YoY and QoQ comparisons when stated. "
        "Separate reported results from forward-looking guidance. "
        "Flag management projections with [FORWARD-LOOKING]."
    ),
    "medical": (
        "Use standard medical terminology. Identify drug names (generic "
        "and brand), dosages, mechanisms of action, and clinical trial "
        "results with sample sizes and p-values. Note study phase and "
        "primary endpoints."
    ),
    "legal": (
        "Identify all case citations in Bluebook format, statutory "
        "references, and legal standards. Distinguish holding from dicta. "
        "Note jurisdiction and court level. Flag any dissenting opinions."
    ),
}

API_URL = "https://api.yt2text.cc/api/v1/videos/process"
API_KEY = "sk_your_api_key"


def process_video(video_url: str, domain: str, mode: str = "key_insights"):
    """Submit a video for processing with domain-specific instructions."""
    if domain not in DOMAIN_PROMPTS:
        raise ValueError(f"Unknown domain: {domain}. Choose from: {list(DOMAIN_PROMPTS)}")

    response = requests.post(
        API_URL,
        headers={
            "X-API-Key": API_KEY,
            "Content-Type": "application/json",
        },
        json={
            "video_url": video_url,
            "summary_mode": mode,
            "custom_instructions": DOMAIN_PROMPTS[domain],
        },
    )
    response.raise_for_status()
    return response.json()


# Usage
result = process_video(
    video_url="https://www.youtube.com/watch?v=VIDEO_ID",
    domain="financial",
    mode="detailed",
)
print(f"Job submitted: {result['data']['job_id']}")

This pattern centralizes prompt management, making it straightforward to update a single domain template and have the change propagate to all processing calls. Store the prompt dictionary in a configuration file or environment variable for production deployments so that prompt updates do not require code changes.

How do custom prompts combine with summary modes for layered output?

The five summary modes in YT2Text each produce structurally different output. When you combine a mode with custom instructions, the mode controls the output structure while the instructions control the content focus. This creates a layered system where you can generate multiple complementary views of the same video.

For a medical conference presentation, you might run three passes:

tldr

detailed

timestamped

The

study_notes

Consider building a pipeline that automatically runs two modes per video: one for quick reference and one for deep analysis. Use

key_insights

detailed

Key Takeaways

• Generic AI summaries lose critical domain-specific terminology, metrics, and structural conventions that specialized teams depend on for professional workflows.
• The

  custom_instructions

  parameter layers domain context on top of any summary mode, steering the AI toward the exact extraction targets your field requires.
• Effective custom prompts are explicit: specify the metrics, terminology conventions, citation formats, and comparison frameworks you expect rather than relying on vague guidance.
• Treat prompts as versioned artifacts. Test them against ground truth, track completeness and accuracy scores, and maintain separate templates for distinct content types within each domain.
• Combine custom instructions with multiple summary modes to generate layered output packages, such as a quick-reference

  tldr

  alongside a comprehensive

  detailed

  summary, from a single video.
• Centralize prompt management in a configuration dictionary or file so that domain template updates propagate consistently across all processing calls without code changes.
• Apply the QA checklist for AI-generated video notes to domain-specific outputs, with heightened scrutiny on numerical claims, terminology accuracy, and citation correctness.