If you start learning LangChain by jumping straight into agents. That's usually where the confusion begins.
You see terms like chains, tools, messages, memory, structured outputs, and agent loops everywhere, but nobody explains how they connect together.
The truth is that LangChain becomes much easier when you learn it layer by layer.
A language model is just a function: text in, text out.
Messages give conversations context.
Tools let models interact with the real world.
Structured outputs turn messy text into reliable data.
Agents combine all of these pieces into a loop that can think, act, observe, and continue until a task is complete.
Once you understand that progression, the abstractions stop feeling overwhelming and start feeling obvious.
In this guide, I break down LangChain from the very first model call all the way to building a working agent, with practical examples and a mental model that actually sticks.
If you've ever felt lost while learning LangChain, this is the roadmap I wish I had when I started.
Most people think AI is about asking random questions.
But the people getting insane results from AI?
They follow systems.
This image explains the โ20 Laws of Claude Promptsโ โ and honestly, these rules work for ChatGPT, Gemini, and every major AI tool.
The biggest mistake beginners make:
โ Vague prompts
โ No context
โ No structure
โ No clear outcome
And then they say:
โAI is overrated.โ
No.
Your prompt decides the quality of the answer.
The best AI users donโt just type.
They think strategically.
They:
โข Give context
โข Define the format
โข Set boundaries
โข Ask for edge cases
โข Refine responses
โข Fact-check outputs
โข Use AI like a system, not a toy
Thatโs the difference between average output and world-class output.
AI wonโt replace humans.
But humans who know how to use AI effectively will outperform those who donโt.
Prompt engineering is becoming a real digital skill in 2026.
Save this.
Study it.
Use it.
Because better prompts create better thinking.
And better thinking creates better results. ๐
Follow me @Tech_by_Shweta
For more
#AI #Claude #ChatGPT #ArtificialIntelligence #PromptEngineering #AITools #Productivity #Tech #FutureOfWork #Automation
City Hall announces another Knicks watch party capacity 5,000 at Bryant Park
Free, registration required
This is in addition to Wollman Rink and Brooklyn Bowl
Looking to #connect with builders on @X.
If youโre into:
โข Building SaaS
โข AI tools
โข Vibe coding
โข Building in public
โข Figuring things out as you go
Drop a quick intro or tell us what youโre working on ๐
Always down to connect with builders!
Michael Wilbon doesnโt see the Jalen Brunson and Kobe Bryant comparisons:
โKobe had a ruthlessness that was shared with very few players in my observation. Bird, Jordan, Kobe. Idk if I put anyone else in that group. LeBron is a nicer guy than that. I donโt put LeBron in that group in terms of ruthlessness. I donโt put Jalen Brunson in it. Not yetโ
๐ Most people treat like a README.
Big mistake.
A great gives your AI: โข Clear project context โข Coding standards โข Workflows & commands โข Team conventions โข
Specific rules
The better the instructions, the better the output.
Most people use Claude like a chatbot.
Ask a question.
Get an answer.
Move on.
That's leaving a lot of value on the table.
The real power comes from using the right prompts.
These 15 prompts can help you:
โ Make better decisions
โ Analyze complex problems
โ Learn new skills faster
โ Create better content
โ Research markets and customers
โ Summarize information in seconds
โ Plan strategies with more confidence
The quality of your output depends on the quality of your input.
Save this for later.
Which Claude prompt do you use the most?
๐ Repost to help others
๐ Follow @MarcelVelica for more AI insights
๐๐๐ ๐๐ ๐๐ด๐ฒ๐ป๐ ๐๐ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐๐ ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐๐๐ฒ๐บ
Most people treat these as the same thing.
They're not.
Understanding the difference can save you time, money, and countless debugging headaches.
Here's the simplest way to think about it:
๐๐๐ โ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ
A language model generates responses based on the context it receives.
โ Single-step output
โ No independent decision-making
โ Limited to provided context
โ Tool usage depends on external systems
Best for: โข Writing โข Summarization โข Q&A โข Brainstorming
Autonomy: โ โโโโ
๐๐ด๐ฒ๐ป๐ โ ๐๐ฐ๐
An agent doesn't just respondโit takes action.
โ Goal-oriented โ Uses tools โ Plans next steps โ Can retry when things fail
Best for: โข Research โข Automation โข Troubleshooting โข Task completion
Autonomy: โ โ โ โโ
๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ โ ๐ข๐ฟ๐ฐ๐ต๐ฒ๐๐๐ฟ๐ฎ๐๐ฒ
AI operates inside a structured process with predefined steps.
โ Predictable flow โ Easier monitoring โ Optional human approvals โ Higher reliability
Best for: โข Business operations โข Document processing โข Compliance workflows โข Repetitive processes
Autonomy: โ โ โ โ โ
๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐๐๐ฒ๐บ โ ๐๐ผ๐น๐น๐ฎ๐ฏ๐ผ๐ฟ๐ฎ๐๐ฒ
Multiple specialized agents work together toward a shared objective.
โ Specialized expertise โ Parallel execution โ Coordinated decision-making โ Handles complex problems
Best for: โข Large-scale projects โข Enterprise automation โข Complex research โข Multi-stage workflows
Autonomy: โ โ โ โ โ
๐ง๐ต๐ฒ ๐ญ-๐๐ถ๐ป๐ฒ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ
โ Need an answer? Use an LLM.
โ Need actions taken? Use an Agent.
โ Need consistency and control? Use an Agentic Workflow.
โ Need multiple specialists working together? Use a Multi-Agent System.
The biggest mistake isn't choosing a simple solution.
It's choosing a complex one when you don't need it.
Many teams build multi-agent architectures for problems a single prompt could solve.
More autonomy means more coordination, more latency, more cost, and more failure points.
The best AI architecture isn't the most advanced.
It's the one that's just advanced enough for the problem you're solving.
What's your definition of the line between a true Agent and an Agentic Workflow?
๐ Share your thoughts.
๐ Save this for later. ๐ Repost if you found it useful. โ Follow for more AI insights.
"I consider myself an originalist in the following sense: The original intent of Congress was not to have federal courts enjoining a criminal president. It was to have Congress impeach and remove that president. That's the originalism I'm standing for hereโthe originalism of Congress reasserting its power to act as a check on the president. That's the reason why they created an executive, is they thought that Congress would have the gumption and strength to exercise its power and duty to remove a criminal from office."
Trump's 0 for 4 on his midterm report card.
He promised to make the economy bigger and better, lower prices immediately, end the war in Ukraine in 24 hours, and stop endless wars. He's delivered on none of it.
i've been using Claude Opus 4.8 + Google Ads MCP to find the winning ad angles hiding inside our clients' accounts...
and results have been absolutely CRACKED
so i've decided to document the ENTIRE system...
covering the exact setup + the 6 prompts we run to surface the headlines and search intent already converting in your account, plus the competitor angles you've been ignoring, all of it invisible in the dashboard right now
here's what's included inside the guide:
> top performer hook extraction
(pulls headline-level RSA data and groups by angle. found a supplement client's symptom-led headlines converting 3.2x the product-claim ones (buried in RSAs, nobody saw them)
> search term intent gap
(finds the gap between what people search and what your ads say. an Australian health brand had "magnesium for leg cramps at night" hitting sleep ads. rewrote to match intent, CPA dropped 34%)
> competitor angle study
(reverse-engineers competitor ads through the transparency center. surfaced a "no bloating" angle 3 competitors had validated and the client ignored. became the top headline in the account in 2 weeks)
> landing page message match audit
(scores ad-to-page alignment and fixes quality score. a home goods client's "office chair for back pain" ads pointed at a generic page. built a dedicated page, quality score went 4 to 7, CPC dropped 28%)
> negative keyword intelligence sweep
(finds wasted spend and converting terms you're not bidding on. on a $94k/month brand, found $4.2k/month in waste plus 12 converting search terms missing from the keyword list. ROAS up 0.4x in one week)
> seasonal & trend angle detector
(looks forward instead of back. seasonal spikes, emerging demand, expansion regions, ranked by revenue impact)
all backed by everything i learned generating $13M+ in the past 6 months at my Google Ads agency
and for 24h, it can be ALL yours for free
like + comment "MCP" and i'll send it over
(must be following + RT for priority access)
what is agent looping
for the last two years we prompted agents one task at a time. that is starting to change
instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met
looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up
at its simplest, looping is one agent working on itself:
> researches
> drafts
> checks the draft against a goal
> fixes what is weak
> runs that cycle again until the work clears the requirements
you are not prompting each step anymore. the agent repeats the cycle for you
the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents
the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met
one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end
you create a goal, and the system runs the loop until it finishes within the reqs you set
open and closed looping:
OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out
this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time
the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine
CLOSED LOOPING is bounded. a human designs the end-to-end path first:
> clear goal
> defined steps
> an eval at each step
> a point where it stops or hands back to you (and feeds back performance data)
the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight.
for most marketing work, closed is the one that pays off today.
> the orchestrator owns the goal
> the specialists own the steps
> the subagents do the narrow work
> an eval gate make sure its not slop
Claude is PERFECT for running a small business without a full team.
It's helped owners cut manual admin and run financial, sales, ops, and marketing tasks from one place. 382,000 downloads in a single day.
I've just broken the system down so you can do the same:
- Connect your business tools once - QuickBooks, Gmail, Stripe, HubSpot, Google Drive - and every skill that needs them can use them from that point forward
- The 5 skills to run in your first week: Business Pulse, Friday Brief, Invoice Chase, Close Month, and the Small Business Router that tells you which of the remaining 26 are most useful for your specific business type
- 31 skills across every function: financial ops, client work, team management, marketing, and weekly reporting โ all pre-built, all running from a single slash command
Want me to send it over?
Like + comment "BUSINESS"
(must be following)