I’m trying to fetch data from the Facebook Ads Insights API, but every request returns the following error:

{
  "error": {
    "message": "(#3) Application does not have the capability to make this API call",
    "type": "OAuthException",
    "code": 3
  }
}

The app has a valid access token, and I can successfully make requests to other Facebook Graph API endpoints.

Example request:

GET https://graph.facebook.com/v2.8/act_<AD_ACCOUNT_ID>/insights
  ?fields=impressions,clicks,spend
  &access_token=<ACCESS_TOKEN>

Has anyone encountered this issue with the Insights API specifically?

I’m wondering if this is related to:

• Missing app permissions
• App review requirements
• Ads API access approval
• Business Manager configuration

What was the actual fix in your case?

I’m working on an AI application that uses a large language model for question answering over internal documentation. While retrieval augmentation has improved factual accuracy, the model still occasionally generates confident but incorrect responses when the retrieved context is incomplete or ambiguous.

A simplified version of the inference pipeline looks like this:

retrieved_docs = retriever.search(query, top_k=5)

prompt = f"""
Use ONLY the information below to answer the question.

Context:
{retrieved_docs}

Question:
{query}
"""

response = llm.generate(prompt)

I’ve experimented with increasing retrieval depth, adjusting chunk sizes, and rewriting prompts, but there’s always a trade-off between factual accuracy, latency, and response quality.

For those building production AI systems:

• How do you measure and mitigate hallucinations beyond prompt engineering?
• Have you found techniques like reranking, verification models, or multi-agent validation to be effective?
• What evaluation metrics do you rely on to determine whether changes actually improve factual reliability?

I’m particularly interested in approaches that have worked well in production rather than benchmark experiments.

Hi everyone,

I’m a student working on a small forecasting project where I only have around 18–24 months of monthly demand data. Since the dataset is quite limited, I’m unsure which forecasting approach would be the most reliable.

I’ve read about moving averages, exponential smoothing, ARIMA, Prophet, and even machine learning models, but I’m not sure if complex models make sense with so few observations.

Should I stick to traditional statistical methods, or is there a better approach for small datasets? Also, how do you validate the model when there isn’t much historical data available?

I’d really appreciate any advice or resources that could help me understand the best practices for this kind of problem. Thanks!

Artificial Intelligence is evolving faster than many organizations can integrate it responsibly. Beyond just adopting AI tools, the real challenge is merging AI capabilities with existing business processes, decision-making structures, and human judgment.

From autonomous workflows to multi-agent ecosystems, AI is reshaping how decisions are made, how teams collaborate, and how strategies are executed. But this also raises questions about governance, interoperability, and the operational impact of AI when it scales across functions.

In my experience, the organizations that thrive aren’t those with the most advanced AI, they’re the ones that know how to blend AI with human judgment and operational insight effectively.

Given the rapid pace of AI adoption, how are you approaching the integration of AI into your organizational workflows while ensuring human oversight, interoperability, and responsible governance? What frameworks or approaches have you found most effective?