Beyond RAG: How cache-augmented generation reduces latency, complexity for smaller workloads
RAG is an effective method for handling open-domain questions and
specialized tasks. It uses retrieval algorithms to gather documents that are
relevant to the request and adds context to enable the LLM to craft more
accurate responses. ... First, advanced caching techniques are making it faster
and cheaper to process prompt templates. The premise of CAG is that the
knowledge documents will be included in every prompt sent to the model.
Therefore, you can compute the attention values of their tokens in advance
instead of doing so when receiving requests. This upfront computation reduces
the time it takes to process user requests. Leading LLM providers such as
OpenAI, Anthropic and Google provide prompt caching features for the repetitive
parts of your prompt, which can include the knowledge documents and instructions
that you insert at the beginning of your prompt. ... And finally, advanced
training methods are enabling models to do better retrieval, reasoning and
question-answering on very long sequences. In the past year, researchers have
developed several LLM benchmarks for long-sequence tasks,
including BABILong, LongICLBench, and RULER. These benchmarks
test LLMs on hard problems such as multiple retrieval and multi-hop
question-answering.
Turning Curiosity into a Career: The Power of OSINT
The beauty of OSINT is that you can start learning and practicing right now,
even without a formal background in cybersecurity. Begin by familiarizing
yourself with publicly available tools and resources. Social media platforms,
search engines and public record databases are great starting points. From
there, you can explore specialized tools like Google Dorking for advanced
searches, reverse image search for photo analysis, and platforms like Maltego or
SpiderFoot for more in-depth investigations. The OSINT Framework provides an
extensive list of tools. If you're interested in pursuing OSINT as a career,
consider taking advantage of free and paid online courses. Certifications such a
GIAC Open Source Intelligence (GOSI) or Certified Ethical Hacker (CEH) can help
build your credibility in the field. Participating in OSINT challenges or
contributing to community projects is also a great way to hone your skills and
showcase your abilities to potential employers. The demand for OSINT skills is
growing as technology evolves and data becomes more accessible. Artificial
intelligence and machine learning are enhancing OSINT capabilities, making it
easier to analyze massive datasets and detect patterns.
Five Trends That Will Drive Software Development in 2025
While organizations worldwide have quickly adopted AI for software development,
many still struggle to measure its impact across diverse teams and business
functions. Next year, organizations will become more sophisticated about
measuring the return on their AI investments and better understand the value
this technology can provide. This starts with looking more closely at specific
outcomes. Instead of asking a broad question like, ‘How is AI helping my
organization?’ leaders should study the impact of AI on tasks, such as test
generation, documentation or language translation, and measure the gains in
efficiency and productivity for these activities. ... While developers already
work at breakneck speed today, technical debt is a persistent issue. The most
worrying consequence of this debt is vulnerabilities that can creep into code
and go unnoticed or unfixed. Next year, developers will expand their use of AI
in software development to significantly reduce technical debt and increase the
security of their code. Technical debt often occurs when developers choose an
easy or quick solution instead of a better approach that takes longer.
Vulnerabilities result when the code is poorly structured, not sufficiently
reviewed or when testing is rushed or incomplete.
A Cloud Architect’s Guide to E-Commerce Data Storage
Latency, measured in microseconds, is the enemy of e-commerce storage systems,
as slow-performing systems can mean hundreds of thousands of dollars in lost
transactions and abandoned shopping carts. Your data platform must be reliable
and highly performant even during fluctuating demand; events like Black Friday
or unexpected social media trends can put a heavy load on your systems.
Infrastructure that supports real-time data processing can be the deciding
factor in staying competitive. These challenges necessitate a modern approach
to storage — one that is software-defined, scalable and cloud-ready. ...
Foundational elements of a modern e-commerce infrastructure consist of
software-defined storage often combined with open-source environments like
OpenStack, OpenShift, KVM and Kubernetes. The challenge for platform
architects, whether building their e-commerce storage platform on premises or
in the cloud, is to achieve scale and flexibility without compromising
application and site performance. Many legacy storage systems, especially
those architected for spinning disks, have performance limitations, resulting
in data silos and expensive and time-consuming scaling strategies.
Demand and Supply Issues May Impact AI in 2025
Executives are asking for ROI numbers on analytics, data governance, and data
quality programs, and they are demanding dollar values as opposed to
“improving customer experience” or “increasing operational efficiency. ...
Organizations have expected quick returns but not realized them because the
initial expectations were unrealistic. Later comes the realization that the
proper foundation has not been put in place. “Folks are saying they expect ROI
in at least three years and more than 30% or so are saying that it would take
three to five years when we’ve got two years of generative AI. [H]ow can you
expect it to perform so quickly when you think it will take at least three
years to realize the ROI? Some companies, some leadership, might be freaking
out at this moment,” says Chaurasia. “I think the majority of them have spent
half a million on generative AI in the last two years and haven’t gotten
anything in return. That's where the panic is setting in.” Explaining ROI in
terms of dollars is difficult, because it’s not as easy as multiplying time
savings by individual salaries. Some companies are working to develop
frameworks, however. ... If enterprises are reducing AI investments because
the anticipated benefits aren’t being realized, vendors will pull back.
4 Strategies To Thrive In A Manager-Less Workplace
One of the most important skills you can build is emotional regulation. Work
can be intense, often frustrating. It’s easy to get caught up in your own
emotions and—since emotions are catching—other people’s as well. Staying
even-keeled pays off in maintaining good relationships with peers and also
keeping yourself clear-headed so you can problem-solve when things go wrong.
You can work on your emotional self-control by learning the tools of
journaling and mindfulness. ... When you communicate powerfully, you navigate
more easily. You get what you need more efficiently, you sell your ideas, and
you build better relationships. All of these outcomes are useful when you’re
on your own to build a case for getting promoted. The best way to build these
skills is to practice. Volunteer to give large presentations and ask for
feedback. Craft your emails and slack messages with an understanding of the
receiver and ask them if they have suggestions for you. ... Your network
inside your company can also provide the emotional support you would have
gotten from your manager. And, when it comes time for you to be promoted, in
most companies you need your colleagues to support you. Look around at your
coworkers to see who are the most interesting, plugged-in, or
effective.
Dark Data: Recovering the Lost Opportunities
Dark data is the data collected and stored by an organization but is not
analyzed or used for any essential purpose. It is frequently referred to as
"data that lies in the shadows" because it is not actively used or essential
in decision-making processes. ... Dark data can be highly beneficial to
businesses as it offers insights and business intelligence that wouldn't be
available otherwise. Companies that analyze dark data can better
understand their customers, operations, and market trends. This enables them
to make the best decisions and improve overall performance. Dark data can help
organizations recoup lost opportunities by uncovering previously unknown
patterns and trends. ... Once the dark data has been collected, it must be
cleansed before further analysis. This may include deleting duplicate data,
correcting errors, and formatting information to make it easier to work with.
After the data has been cleansed and categorized, it can be examined to reveal
patterns and insights that will aid decision-making. ... Collaborating with
cross-functional teams, such as IT, data science, and business divisions, can
assist in guaranteeing that dark data is studied in light of the
organization's broader goals and objectives.
The difference between “data deletion” and “data destruction” is critical to
understand. “Data deletion” simply means removing a file from a system, making
it appear inaccessible, while “data destruction” is a more thorough process
that permanently erases data from a storage device, making it completely
irretrievable. Deleting data isn’t enough. Without proper destruction
protocols, “deleted” data remains vulnerable to breaches, regulatory
compliance, and data recovery tools. ... A well-defined data destruction
policy is your organization’s first line of defense. It outlines when, how,
and under what circumstances data should be destroyed. Without a formal
policy, data is often overlooked, forgotten, or destroyed haphazardly,
creating compliance and security risks. To implement this, start by
identifying the types of data your organization collects and classifies, such
as PII or proprietary records. Define clear retention periods based on
regulatory requirements like GDPR or CCPA and document the necessary steps,
tools, and roles for secure destruction. Assign accountability to ensure
oversight and follow-through. A formal policy isn’t just a “nice-to-have.”
It’s a compliance requirement for many regulations, including GDPR and
CCPA.
Can GenAI Restore the ‘Humanity’ in Banking that Digital Has Removed?
Abbott is not arguing for turning customers directly over to GenAI — not yet.
Even the most-advanced pioneers his firm works with aren’t risking that. ...
Abbott believes GenAI, as it becomes a standard part of banking, will play out
in a similar way. Employees will adapt, often more slowly than anticipated,
but they will change. This will lead to shifts in the role of management
vis-à-vis employees empowered by GenAI. Abbott says this will likely take a
similar path to that seen as banks adopted agile development. Young people
came into the bank using the tools, just as many are already experimenting
with GenAI. Banking leaders liked the idea of their organizations "doing
agile." But what Abbott calls "the frozen middle" management tier had to grin
and plunge into unfamiliar turf. "That frozen middle will have to thaw out and
find a new way of working," says Abbott. Bank leadership must help by
providing tools and opportunities for trying it out. One of the biggest early
challenges will be tempering the GenAI tech to the task. Abbott explains that
GenAI can be tuned to be "low temperature" or "high temperature," or somewhere
in between. The former refers to GenAI working with tight guardrails, such as
in sensitive areas like dispute management.
Federated learning: The killer use case for generative AI
Federated learning is emerging as a game-changing approach for enterprises
looking to leverage the power of LLMs while maintaining data privacy and
security. Rather than moving sensitive data to LLM providers or building
isolated small language models (SLMs), federated learning enables
organizations to train LLMs using their private data where it resides.
Everyone who worries about moving private enterprise data to a public space,
such as uploading it to an LLM, can continue to have “private data.” Private
data may exist on a public cloud provider or in your data center. The real
power of federation comes from the tight integration between private
enterprise data and sophisticated LLM capabilities. This integration allows
companies to leverage their proprietary information and broader knowledge in
models like GPT-4 or Google Gemini without compromising security. ... As
enterprises struggle to balance AI capabilities against data privacy concerns,
federated learning provides the best of both worlds. Also, it allows for a
choice of LLMs. You can leverage LLMs that are not a current part of your
ecosystem but may be a better fit for your specific application. For instance,
LLMs that focus on specific verticals are becoming more popular.
Quote for the day:
"Too many of us are not living our
dreams because we are living our fears." -- Les Brown
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